{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # 矩阵操作\n",
    "import pandas as pd # SQL数据处理\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "import matplotlib.pyplot as plt   #画图\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.363625</td>\n",
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       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant     dteday  season  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "0        1 2011-01-01       1     1        0        6           0           2   \n",
       "1        2 2011-01-02       1     1        0        0           0           2   \n",
       "2        3 2011-01-03       1     1        0        1           1           1   \n",
       "\n",
       "       temp     atemp       hum  windspeed   cnt  \n",
       "0  0.344167  0.363625  0.805833   0.160446   985  \n",
       "1  0.363478  0.353739  0.696087   0.248539   801  \n",
       "2  0.196364  0.189405  0.437273   0.248309  1349  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入数据文件\n",
    "dpath = \"H:/Python36/\"\n",
    "data = pd.read_csv(dpath+\"day.csv\")#读取\n",
    "data = data.drop([\"yr\",\"casual\",\"registered\"],axis=1)#删除不需要的特征\n",
    "data[\"dteday\"] = pd.to_datetime(data[\"dteday\"])#时间格式转换\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
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       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
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       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dteday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
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       "      <td>2011-01-01</td>\n",
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       "      <td>1</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-02</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            instant     dteday  season  mnth  holiday  weekday  workingday  \\\n",
       "dteday                                                                       \n",
       "2011-01-01        1 2011-01-01       1     1        0        6           0   \n",
       "2011-01-02        2 2011-01-02       1     1        0        0           0   \n",
       "\n",
       "            weathersit      temp     atemp       hum  windspeed  cnt  \n",
       "dteday                                                                \n",
       "2011-01-01           2  0.344167  0.363625  0.805833   0.160446  985  \n",
       "2011-01-02           2  0.363478  0.353739  0.696087   0.248539  801  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将日期设置为数据索引\n",
    "data = data.set_index(data[\"dteday\"])\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
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       "      <th>dteday</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2012-01-01</th>\n",
       "      <td>366</td>\n",
       "      <td>2012-01-01</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.370000</td>\n",
       "      <td>0.375621</td>\n",
       "      <td>0.692500</td>\n",
       "      <td>0.192167</td>\n",
       "      <td>2294</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>367</td>\n",
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       "      <td>0.381304</td>\n",
       "      <td>0.329665</td>\n",
       "      <td>1951</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
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       "      <td>0.441250</td>\n",
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       "      <td>2236</td>\n",
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       "      <th>2012-01-04</th>\n",
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       "      <td>2012-01-04</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.107500</td>\n",
       "      <td>0.119337</td>\n",
       "      <td>0.414583</td>\n",
       "      <td>0.184700</td>\n",
       "      <td>2368</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>370</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265833</td>\n",
       "      <td>0.278412</td>\n",
       "      <td>0.524167</td>\n",
       "      <td>0.129987</td>\n",
       "      <td>3272</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "            instant     dteday  season  mnth  holiday  weekday  workingday  \\\n",
       "dteday                                                                       \n",
       "2012-01-01      366 2012-01-01       1     1        0        0           0   \n",
       "2012-01-02      367 2012-01-02       1     1        1        1           0   \n",
       "2012-01-03      368 2012-01-03       1     1        0        2           1   \n",
       "2012-01-04      369 2012-01-04       1     1        0        3           1   \n",
       "2012-01-05      370 2012-01-05       1     1        0        4           1   \n",
       "\n",
       "            weathersit      temp     atemp       hum  windspeed   cnt  \n",
       "dteday                                                                 \n",
       "2012-01-01           1  0.370000  0.375621  0.692500   0.192167  2294  \n",
       "2012-01-02           1  0.273043  0.252304  0.381304   0.329665  1951  \n",
       "2012-01-03           1  0.150000  0.126275  0.441250   0.365671  2236  \n",
       "2012-01-04           2  0.107500  0.119337  0.414583   0.184700  2368  \n",
       "2012-01-05           1  0.265833  0.278412  0.524167   0.129987  3272  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照日期将数据分为训练集和测试集\n",
    "data_train = data[\"2011\"]\n",
    "#data_train.head()\n",
    "data_test = data[\"2012\"]\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "h:\\python36\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据探索\n",
    "#2011年一年内的租车人数的分布,受特征影响分布呈现差异性\n",
    "fig = plt.figure()\n",
    "sns.distplot(data_train[\"cnt\"].values, bins=30,kde=False)\n",
    "plt.xlabel(\"cnt\",fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 租车人数随每天时间变化的散点图分布\n",
    "plt.scatter(data_train[\"dteday\"].values,data_train[\"cnt\"].values,color='purple')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#季节特征\n",
    "sns.boxplot(x='season', y='cnt', data=data_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#月份特征\n",
    "sns.boxplot(x='mnth', y='cnt', data=data_train)\n",
    "plt.show()\n",
    "#时间特征对租车人数有影响\n",
    "#由于日期，月份，季节有一定的时间相关性，故可只取其一为主要时间特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 节假日\n",
    "sns.boxplot(x='holiday', y='cnt', data=data_train)\n",
    "plt.show()\n",
    "#是否节假日对租车人数有一定的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#星期\n",
    "sns.boxplot(x='weekday', y='cnt', data=data_train)\n",
    "plt.show()\n",
    "#星期对租车人数影响不是很大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#工作日\n",
    "sns.boxplot(x='workingday', y='cnt', data=data_train)\n",
    "plt.show()#是否工作日对租车人数影响不大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#天气\n",
    "sns.boxplot(x='weathersit', y='cnt', data=data_train)\n",
    "plt.show()#天气对租车人数影响较大，恶劣天气下租车人数可能为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 气温\n",
    "plt.scatter(data_train[\"temp\"].values,data_train[\"cnt\"].values,color='purple')\n",
    "plt.show()\n",
    "#气温越高，租车人数越大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#体感温度\n",
    "plt.scatter(data_train[\"atemp\"].values,data_train[\"cnt\"].values,color='purple')\n",
    "plt.show()\n",
    "#体感温度与气温对租车人数影响趋势相似，考虑只考虑其一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZMVXoZOBsFpUbYd/n+hom6L7P9YWKAHbjFjTue6PywvGaPHu29iiPnzs+56smsiOuqyPVWgyBxi4kqPxk/TiVwFYEzTUUp3EsEuZOeO9w8hrcFQZRAQmlxq377R3oVaoQ4jAWhslKGaSCUaKhQqmsqnheVxUbEFaH72Tu+FxwdLJFGLWdSu3nZf+wr+vlDKByt81rcFcYRAAIpcXLiDg1PKVUIcS1ItTxSHIeFyqxGwF9n6t5/9k1fOMwagamhA5Ap+9hs7T6CdOuq7u0jbqmpDDPAhEAQmkJneohoxWhPXGpgruotRZYZU90QHD5RZXaQ9UepixmJAiR/P5VwlRXyNrHAuWsFSACQCgtQakeslgRqtwRx3eNK/Xn6wfX16OAAe8avnZOn/0D+7F+cH0kt1l7Uk0iHXTW6hZdgVE0d1ExAgulRZl9UmEsTPqL7pV99MDNB3DP6nt8J9wXHn+h4bWfqooXGRN7J7DyspWRc9qETVEdRF7ULXHXmTYBEQBCafFL7OXlUaSbwjkqqkC0ICPw3PG5hr7orKZ/9t2fRRZyQbUWwmIb2E2fSItWCwAQFZBQYsLofsOkcA5L1OpdTpweSloGWw6nJ3fiVIOo7CVhiXo/01TJFK0WACACQCg5upNgUnlkms2ZY+OchJI02Lr76zf52y6lum6sYe9nkkLZi6LVAgBEBSQIWiS1+tP1jw9CFfilLMlI4ap7AbXj9w/s1+7v3PE5dJzTgf5H+2vRyI+eiUZWEeZ+pq2SKVotAEB2AELBSEolkNTqL6wAae1oBTM31Dbwm4Q+8cAncOCzB7B0eqnxDUao1bK92g6r7nGvyoPyAnV2dWo/Q9XuJqksqEV0F5UdgFAYkvTSSGr1FyRAKqsqDYbaLQ9twfVfv17beNuztQeX/+Hlnu+FWS0H7VT8ahbbLqhOw7nqfnZv6tZ+hqrP1K2fHIVm042YhuwAhMKQZL73pFZ/fgbb9hXtgemWdZg+Mq18T3cH4necXVQlyObgpaN3prpoq7Thhcdf0H6GfknfnETdFRbN598LEQBCYUjaSyOq10zQNYEzBlvbqyZsWgQ//Mavq8KqrPTOHeSsRexXjN7GPZnbKaUBf2Ox1xhURW+c7qk6hmKviR4IjqYuAqICEgqDMrArL14aBJx78bnof7Q/VvWC3/i7N3UHnn/49sOek3NLWwvOOu8sjG6rqXfsWgBB2JN5GAO417V11HJBhmKV2nBs+1jhfP69EAEgFIY8emkkabewA9f81DJ+6iH7GhMPeNflXjq91JD7/9QvT2n1yxZIYXZmp355atk90akBEbQrVAkI1W4klTKbKaItAIiolYh+RETfsV5fSkTPEtE0EX2LiDqs9vdYr49a769zXONLVvtLRPTxuAcjlJs4isKkTVKujA2CxYegSdgvB5GbpYUltJ/d7uvmaRt696zbo31d+9pe9yTIKBu0KwytHozgPmsyYXYA2wH81PH6zwB8jZm7AbwD4Far/VYA7zDzZQC+Zh0HIvoggBsBfAjAtQDuJ6LW5rovCI3kzUsj6/gCP/VQdaQaesW7cHKhofpYZVWlFhBmCWQ7V3+UlXSUexK0K1SN3+7zMhiFUgNpCQAiuhjAZgD/w3pNAH4XwLetQ4YBXG/9vcV6Dev9DdbxWwA8xsynmPlnAI4CuCKOQQhCXonbbqGj9nGisgHYO4iwVFZWGoTwzrd3YufbO+sCefrIdPTAN0boHExBu0KVgNh470blDiXPqR/c6O4A9gDYCcCOJlkF4BfMbJvwjwG4yPr7IgCvAYD1/qx1fL3d45w6RDRIRBNENPHWW2+FGIogmIVO8jg/f/iwied01T5OVDYA3x0EAa1nRdu860ye1ELKbKNRbCROgbTh7g0Y3zVev68AlAJClewuN04FGgQKACL6BIA3mXnS2exxKAe853fOmQbmfczcx8x9a9asCeqeIBiJrnHXa4XaoCYJYRiOklYiigqq/5F+LJ5a9Hxv7sScr+DTmTzX/9F632yjUW0kqmcCwFNtmEengrDo7ACuBvBJInoFwGOoqX72ADiPiOw4gosBvG79fQzAJQBgvd8J4ISz3eMcQSgUYYy7bruFl5pEZ9KLopoIq4LqXNtZWx2rdOcrK76Cr3tTt9JITK2ESzdciukj04ExBbpjdQojrzxGbpdQp+AC1LuDohAoAJj5S8x8MTOvQ82I+11m3grgewA+ZR02AOCg9fch6zWs97/LzGy132h5CV0KoBvAD2MbiSAYRNDK2m+VHNUwHFY14beaDVr9qt4HoJxkqyNVTA1PLdv3255DZ513Fmb+fqZBeKiEhc5Y3St+v1rPYXcHRaGZSOAvAniMiP4UwI8APGi1PwjgESI6itrK/0YAYOYXiOhxAC8COA3g88zsvY8UhJzjlzwuKDo1auI5rToABIDPFGAHvIvHB6W+UL2vWrnPzswqVVQLv661efre28pjx9ytElzOugph6hR0dnUmmkbEZKi2ODeTvr4+npjwDkIRBJPxyvMflDOnc20ndryyw/fcoMnIndage1M3po9Me07iYT+nOlJtyN1TWVVZlqtImeVzbWdtBxNxurHPV+XkiVpXwR7v6LZR774RMLQ0FK3TGUJEk8zcF3Sc5AISBE3CJAfzW0H7rZL9zgXOrNbt1AhzJ+aWrdx1V6yqVe/oTaMY3zW+TFgcuPlAQxrqueNzOHjLwYY+e+1C7BV71AI1tmAMOxYV1ErgJW64b0qhXCCPHy9EAAiCA9UkH6X6lD0Z29cc3VabWFWJ1ZyTjXsid3++83ydvniNy8+m4L7m+K7xhsnfZnF+sUFNEqQ6CrtK1/W60TUKq3Y4foKryIgAEAQLv0k+qo7Y65qtHa1oaW/RLuqi+nzdvqjGpRJEXtf0FRau91S7EN1SlZVVlWU7myBUdpOGY3wyrBax2IsOIgAEwcJvktfx6vGaPLyuuTi/iMqqCjrO6dCebHRWuKpjVONqq7ShfUW7r2Cxr+k7wTJwV9tdWmmsbeGgshVUVlWw8+2dyv6oCKqroGM/SSLdt+mIABAEC79JPqpXj+qacyfmQk10Witchb7arw/9j/T7rsjta264e8MyG4AT2+Nm9tVZjN40irHtY3UDsZdwVKlcNt670XeMKuKuq1CGYjCAeAEJQh0/DxbVhBXk1QN4pxB2GjZ1JpsgLxe7LzNPz2By3yR4kUGthPWD62teQBp9CPIIcnsBBdG+or0e1ex1XcBMlUszXlimoOsFJPUABMHCL/jJL6mYsjj5zGxgQFXUlBHuLJv25D+xd6K+GudFxsTeCay8bGVgH2w1kV1P1yvqtWdrDzbeu1GZp8fNwskFTO6b9LWd7HhlB/of6QeAemGZrNMtJ5Wi20RkByAIDsJu/asjVaUPudOvX3VNv11HkOujG1sP74ZaCTcM36Dl3QT4r3bDZBr1xfKv99zZuILV0l51727ZrYxXCIpHMAWJAxCECIQ1BCoLphDqK2y/a0ZJ+6ASKH5F0lV90PVuckbZhkEVkWvbFjy9m6zDs6rDq7S30Bl1XlFqBIsKSBCaQDlRs97EEDYZm5/KyFbfuFG1A3oCKEqaaaC2k1g/uN5X/RTk3bRwcgH7B/anqhbyUtu501HYfcu7WkgEgCA0gV/WTB3Cphz2W7GvH1zveY6qHdATQEExCO0r2tH/aD/6H+1fZiPZfP9m34yaOpG2vMix1UnWwcveU9TiMKICEoQmiBpB6lTjVFZW0FZp0wp+8luxb75/MwAs8wKy26P232+Sc+vpVUFWqvFoJbBD+onZ3H1W2mpynipCBIAgNEGUCFKvtA7tK9rR/0h/4AQXlCl08/2blRO+nzHar//Kz4xgqHazLDrYQ9Vik+VqW0dQ5jF2QLyAhMIR5ouYxZc2iudPgxHWIz1ykI96sxlG0/KLr45UsX9gv7fhOAaB0wx+/yumxQ7oegGJABAKRZgvYlZfWqWboSL1cByuks26m+oKyigC1SuFtTt4rLWjFR3v7QidIygt4nTnjQNxAxVKSZikbVkVAQlb8EXlKhk0uTgn1mbVKjrusVEypnqdMzU8hd6B3nodg8rKCk798lQ9AtlEF8yoVdyyRryAhEIR5osY15fWr7yjF2E9f5SRxj5umW53URVeQifseGyiRNCqzpk+Ml0vxdhxTseyHESmuWCGdec1BREAQqEI80WM40urm8rBTVvlzOa7sqriq3aK4t+vUyDFS+hEHQ8QTaDqnJOH1XVYoW4KIgCEQhHmixj2S+u1MtZZ9TrPu2f1PTh4y8GGhGqn5077jskvwleF7+To4Y9v00wenCgCVeecPKyu/XJFmYzYAIRCEcYtM8yxKv22apXtrBOgquRlE2R36FyrdsNUEdV10y+xXRBRYiJ0zslLta481hMQASAUjjBfRN1jVStjVa4baiHlDsELvwk2qYnVTXWkqvTD11ltR4mJ0DmnrNW60kDcQAVBA78MkaqqWkHVtpyE8ejx8kFXZfoMM2kqM30StILUBHMQN1ChkEQN3Go24MtPpbLh7g2ewUt+OwQnOuoM1U4lyPUyzBj9EtsJxUSMwEJuiOqh0oxni01QsRheUhtq3ee1tLcsK+YSdXUdZ/ESPzVPmsnYhPSQHYCQG6IGbsUR8BWkhw7aISSlv47TRdIvMdvCyQWM3jSK8V3j9d1KHGPKY/6cIiECQMgNUSe7uCbJsFktnTuEqJNa0AQZNqrYD/u6ozeNKo+ZfXUWB24+ACLC4vxivS1KZG6UyGEhXgJVQER0FhH9kIimiOgFItpttV9KRM8S0TQRfYuIOqz291ivj1rvr3Nc60tW+0tE9PGkBiUUk6j+4Gn4kSfhB66jugoTy6AT4duztSewlsHSwlJ98reJonYqU+1dU9GxAZwC8LvM3AvgtwBcS0RXAfgzAF9j5m4A7wC41Tr+VgDvMPNlAL5mHQci+iCAGwF8CMC1AO4notY4ByMUm6jRlmlFadpFzoeWhrDjlR1Nr2J1JkhdwRPGDuJZEUuDsDuqPET4Fp1AFRDX/ETftV62Wz8M4HcB/HurfRjAlwHsBbDF+hsAvg3gL4iIrPbHmPkUgJ8R0VEAVwD4QRwDEYpPVH/wvPqR606QOiqmMHaQZTn6NQm7o4pTfSVEQ8sGYK3UJwFcBuAvAfwjgF8wsx3DfgzARdbfFwF4DQCY+TQRzQJYZbU/47is8xznZw0CGASArq6ukMMRik5UfXraUZpxGDfjnCDDrrbt++WVirqlvaXBBgBE21GFDVYLG+8Qt4G5iAZrLQHAzIsAfouIzgOwH8AHvA6zfntlqGKfdvdn7QOwD6gFgun0TxBMIi7jZpwpEKIKE9Xuyast7GQYRyqOmadnGmoHBLU7Pzds4aAiGqxDRwIT0RCAkwC+COBfWKv8jwL4MjN/nIietP7+ARG1AfgnAGsA3AEAzPxfrevUj1N9lkQCC3kkzuIgca06TatYFRbVPVWm4lC0288g7P0wreBLELFFAhPRGgALzPwLIqoA+D3UDLvfA/ApAI8BGABw0DrlkPX6B9b732VmJqJDAL5BRP8dwL8E0A3gh6FHJgiGE6dxMy7VVV7tIDaqexc2U6p9nbCxIUU1WOuogC4EMGzZAVoAPM7M3yGiFwE8RkR/CuBHAB60jn8QwCOWkfcEap4/YOYXiOhxAC8COA3g85ZqSRCMJcoK3FTjZh6zVdqo7mnoHYD1DMJO6KY+02YJdANl5ueZ+SPM/G+Y+cPMfJfV/jIzX8HMlzHzpy3vHjDzP1uvL7Pef9lxrbuZ+V8x8/uZeSy5YQlC80RNIWF6cZCoFb+yRHVP1w+uD9VuP4OwsSGmP9OoSCSwICgI8sNX7QzSVreUwZjpd0+7ru4K1Q6EN7B7fX73pm6M7xrH6LbR3KnUbCQdtCAoCJMCOi6DaliVU9GNmUnSjIHddKO6pIMWhCbx0zs3m1zOiyir87C7lKIaM6OgYxNRCYk4EgyagAgAQVCgUhMElYGMSpRJRTmhu0pW2q8rKyueZSnzbMxMqkaEn0AuiiCVegCCoECVZ0eVLK3ZSTTKpKL6TNUuBUAmxsykDM9J1ojwE8h5KFSvgwgAQfDBK8FbUh4hUSYVVV9UfvBzJ+Ziz1p0b3QGAAAWHUlEQVQaRBwFeVREzSiqc56fQC6KV5CogAQhJEl5+URJ/aDqiyqRW2dXZ+rxAEnqy5OsEeHn+x/lf8DEXEIiAAQhAklMos1kO/U6Jq48Qs3iN9kmVqtZo0ZE0HlBAjnM/4Cp7rciAATBIIqY+kE12VZWVpqeFKMmzNM5L857aKrXkMQBCLnGxG210IjKZ76t0ubtkRQyJiEpL6A4UcaUEDC0NBT750kcgFB40tpW60wUJgkik/oCqFfSo9u8aw/HWas5ifOiYGouIREAQm5JY1utI2RME0Qm6pq9Jls/Q3WWJCFA46ztECciAIRESXI1mkYwjo6QCTqm2ZQD9YmSUFcjqCb2uIViks+ve1M3Jh6YaFCNZD0p+hWemT4y3ZAHyPk66L6YZJNxIgJASIykV6NpbKt1hEyQl0vUe7BMd+7SIXtN7HEKxSSfX3WkiqnhqcYxEdA70JvppKgSoE5BNfvqLCb2nrFN6t4XP5VTVmo7CQQTEiNqkI4uaQTj6ARn+R3TzD3wOteNe2KPM0I1yefnOTYGpo9MN33tZlAKygBfmWbuS5KBckGIABASI2kVjSpVQ5wrJx0h43dMM/dA5xj3xB6nUEzy+ZmaS6eZ3WPUvie9UPJDBICQGGnkS/FK1RAnOkKmZ2sPegd6Qa0EoJaHx1ZlNHMPgo7xmtjjFIpJPj9Tc+l4CVCQ3rlR+56lMBQbgJAYpno+hCXIXdDWZ9v5d3iRMTU8ha6ru5q6B17n2obgzrVqPXFc7o1JPj9T/zdUhV+mhqd81XHN9D1LF1ERAEJimOr5EDd+W3g7oCnKPUjj/vkZH5P8fJP/N7wEqLu6WFgvID+yFIYSCSwIEWlw0fQioSjPuAhT1erw7YcxuW8SvMigVsL6wfXYfP/mtLtcWOL2ApJIYEFIEK/J003W+uwgdGMGDt9+uMHtkRe5/jopIWBaNHPSpJ2h1UaMwIIQgSAXTRP02UHoGh8n9016Hqdqb5ZmirwkUXSmyMgOQCg8OqX/wq42fat0+Rho4yKOFbKu8VFVXEbV3ixRopn9gtbsa5ZlNxEGEQBCoamOVHHwloNYnF8EUJsYDt5yEADqaRqiRLsqJ8+QmSyjEFeErq7xkVrJc7K33V7jJopbpEpojG0fw+m508blRjIFUQEJhWZs+1h98rdZnF/E2PYxANGDcLIsCRhX4JBuzMD6wfWe56vavQijnlHWOW4h5fkq4TB3fC6zIKs8IAJAKDRe+ead7VGDcOzJs7KqUm9rq6SzoY4zcMiucdzZ1YnZmVmM7xpfNrluvn8z+m7rawh067utT9sAXB2p4sDNBxp0+gduPqAUAp7BWLBUTgqbQFiDe9YRx6YQKACI6BIi+h4R/ZSIXiCi7Vb7SiJ6ioimrd/nW+1ERPcR0VEiep6ILndca8A6fpqIBpIblpAVeTPENRuRenrudP3vueNzqeRwiTOKVtfguvn+zbjz9J0Y4iHcefrOUN4/Y9vHsLSw1NC2tLBU34W5ce9MvFRN7lW8akfmFNBOTPfQSgudHcBpAH/CzB8AcBWAzxPRBwHcAWCcmbsBjFuvAWAjgG7rZxDAXqAmMAAMAbgSwBUAhmyhIRSDLJNaqVBNAHZ7M6qcrHK4BPVZVwhXR6rYP7A/8TEE7cK8cKb44CVvY7NzFa9SZ228d2Piqrq8LXqcBO5ZmfkNAG9Yf/+KiH4K4CIAWwB8zDpsGMD3AXzRan+YaxFmzxDReUR0oXXsU8x8AgCI6CkA1wL4ZozjETLExLqnG+/diAM3H2hYgba0t2DjvRsBNBeRGlUV06wHj1+fdQ3E9nEqTx6TVCS63kp+vvRJeQGZWoBHl1BKSyJaB+AjAJ4F8D5LOICZ3yCiC6zDLgLwmuO0Y1abql0oCCZmeNSZ4KMG4UTJ4RLklaSLV5/tFb17UvcSwkFxDHGqSCqrKp6rfdXuzE2zqRKSDLIycdETBm0jMBGdA+B/AdjBzL/0O9SjjX3a3Z8zSEQTRDTx1ltv6XZPMICkMzyattWOoj4K8kqKStgVvZ9QjltFsvHejWjtaG1oa+1ore/CvHA+6/Fd4+gd6E007XdUTFz0hEFrB0BE7ahN/iPMbFdy/jkRXWit/i8E8KbVfgzAJY7TLwbwutX+MVf7992fxcz7AOwDarmAtEciZE6SSa2ibrWT3KJHUR/56cN3t+yOrKIIu6JX7V6olWKfXMPeJ69nNjU8taxfJqSLMLXYuy6ByeCIiFDT8Z9g5h2O9j8HcJyZv0pEdwBYycw7iWgzgC8A2ISawfc+Zr7CMgJPArC9gv4fgPW2TcALSQaXP5L6Uu5ZtydS4FXU85JiN+0OPEaVkM33ui27lVWrvK4XJhFc2ug8M1P6H9SPrIRUnMngrgawDUCViH5stf1nAF8F8DgR3QpgBsCnrfeOoDb5HwVwEsDNAMDMJ4joKwCes467y2/yF/KJW99qb+Wb/QKE2Wo7v3SqSTGLLbquyiqKDjnsir7ZdMxJTmw6z9oU3buqfsD4rnGM3jRar98AmGkg1vEC+r9Q18RZtre3vH8+r7jWQwAeCtNBIb/EqX7R3WrrZOn0Oi8NwrhWhhVQKvWb1+Tvnrz7H+kP9TyS9nzRedYm6d6di55l/3+uBYhpBmKJBBYSI04/eV2Dq04h9awydYaZnMIKKN20DnHEaiQd/6DzrE0tKanz/2eSgViSwQmJEXfKAiBYZeF7bUKietggtYhqZetUEwDRBZSOu2McqpOkV986z9rUkpI69yBrIeVEBICQGHF7SOhMcGlm6XRO+JWVFZz65al6wJmXWkQ1afUO9GqVF4xD7x7H5B01/iFM34OetaklJZVC3sIEIeVEBICQGKqi5t2bulP9zCS+dG5dr5d7p3tl3cykFZfePQ6hHOYeV0eqGNs+1nB/4rIZZFVFy4uG8qCuHZ39Oo06EWERASAkRs/WHsw8PYOJBybOfCEYmBqeQtfVXYl8EdJaGeroeoHlK+uok1ZcXi9xCEjde+xnkLdz9Zs0GUbF0/Br8KTvRARAATAhIEbF9JHp1D0h0lgZ6qpMoqq73M9UpVYIq3ePS0BGtTc4mTs+h+pI1Zj/1ah4jpOD1Y4mfG9FAOQc05NRmeSu50T15dP9UgbpeoFauoP5d+dDR/h6PdNlagVHP8KSlupE5xmb5BIZlSj/46Z8b8UNNOdklZJYFxPd9VSukIdvP6ztIqmyY7Sf3Q5QLdEZM9d03yHdLVUrSq9onPl35zPPiaRC5xlnvRCIgyj/46Z8b0UA5BxTV9g2WZZOVKH68k3um9T+Uk4fmfa89orVKzC0NISOczqWFUGxrxWU1E757Hh5Bs2oRWjSSKynquzlRDVJmpb4z48o/+OmfG9FAOQcE1fYTnQDlNJEpbpRZtL0OF75BX51FtWRqu/77l3G6LZR7KYzE53yma7tRMc5HcvaF04uYP/Afu3JMq3CPQ3PHli2g/HzHDKtsJAfUf7HTfneBiaDyxJJBheMKUmx8oROQjYn1Eq48/SdDW2qhGVA7f63Vdo8XUOplZSCxj63d6AXU8NTns90dNuoMr+R+1jV888qQZ6ufSXO/plgaFX1K8nvbZzJ4ASDMTUgpkh4TdieMQ4WCycX0FZpQ/uK9mVf8CDX0YWTC5g+Mo3r9l3XEGQGAKPbRkEt/gLEvoafcTUJ9YPORKtrfI6rf82kEE/6+2TK91YEQAEwKSAmDwStwt3UVRgu2iptygl97sQc+h/pX/YFrwcL+TA7M1t/pu5JTLfffpNl3BHacXu0xNW/KLETaXrnmPC9FRuAUDrWD673bL90w6XLjHlOV05bv25PEn5FzTu7OtGz9Uxh8x2v7EDP1h6tKGjnRKfypadWAsj6HXANN3Eb5uP2aImrf1F2EqZ456SFCAChdGy+fzP6buurT57USui7rQ+f+T+faTDmqVw5x7aP+apy/CYrlfdQHULDuarJipcYQ0tDuGH4htCTZdyG+bhVSnH0rzpSBbWEF46meOekhaiAhFLSdXVXPQHbuRefi66ruwA0bsv3rNuzbJW/cHLBv/RiQOh/ULbSvs/1aWUQtSexqLrkONUPSZRFbKZ/fvWRg4Rj3ks8hkV2AELp0HUzDLvqs330R7eNKt0xVRMJtRL6H+nH5vs3N7TrqEO8VE1pYlqsh5/aLGgnYdpYkkYEgFA6dPW8YVZ9Le0tmP/VfKBQUU0wNwzf4DkxmRhH4ca0PvqpzXR2RiaNJWkkDkAoHcoC6gQMLQ3VX+qWl7R1zby0/KJevuum+qb7kac+ZxXnYBISByAICnT1vG79uioAy2vit/FajZrg/hcGUxKX6WJqtTATERWQUDrC6Hmd+nVVPIAfRTAe5s01smxqnGaQHYBQOqJ6znRv6sbEXn2VZBqrzjRUM2m4RuqMI8xY87bLygoRAEIpiTJBBPrwO9DxOGmWtFQzSbtG6owjb2qovCAqIEHQRHfF6+fVEycq1czY9jHta+ikXQ7rGhk2lbOOiilONVSeUk0njQgAQdBEteKtrKpkom9WCSS71GIQuvEQYXTqUVI566iY4k4Ql5dU00kjKiBB0ETlXbLx3o2ZqCH8ylLqlFoMkyxNV2UWJQGbjoopywRxRSZwB0BEDxHRm0T0E0fbSiJ6ioimrd/nW+1ERPcR0VEiep6ILnecM2AdP01EA8kMRxCSI07vkjjUEFEqTukc04xxN8o1dVRMWSaIKzI6O4C/AfAXAB52tN0BYJyZv0pEd1ivvwhgI4Bu6+dKAHsBXElEKwEMAehDzZt6kogOMfM7cQ1EEHRpxnOmWe+S6kgVY9vHGnIMRTVo9mztWXYtG52VcRLG3SjX1PHK8jqme1M3xneNY3TbqPZzTCPXT56C5rQigYloHYDvMPOHrdcvAfgYM79BRBcC+D4zv5+I/sr6+5vO4+wfZv4jq73hOBUSCSzoovuly7KCWnWkioO3HMTi/KLn+1ErXkUdTxL3Iq37G/Vzku6fKRX6dCOBoxqB38fMbwCA9fsCq/0iAK85jjtmtanaBaFpwhj2sgxqGts+ppz8gWhqiGbUUkkETKUVhBX1OSbdv7wFzcVtBPZKwM0+7csvQDQIYBAAurq64uuZUFjCGPay1AH7FZABoqshmlFLJREwlUYQVjPPMcn+5c3GEHUH8HNL9QPr95tW+zEAlziOuxjA6z7ty2Dmfczcx8x9a9asidg9oUyE+dKpJtmsUzZIrppwmPocTe2XiqgC4BAA25NnAMBBR/tnLG+gqwDMWiqiJwFcQ0TnWx5D11htgtA0Yb50WeZ7t+sFuKGW5KOGi4apeftN7ZcKHTfQbwL4AYD3E9ExIroVwFcB/D4RTQP4fes1ABwB8DKAowD+GsDtAMDMJwB8BcBz1s9dVpsgNE3Y5G69A70N5SB7B3pTmXw33rsRLe2NX7mW9hbc8HDyUcNFw9SEb6b2S4XUAxAKQR68gML002SKMAZd8jpWXS8gEQBCqZBiIc2RtQBNkzyPVQrCCIIHefPSMAHnKphaaFmx9aKmUihD2ggRAEKpSCMStEi4V8Huyd+miAI0zcVCVqomyQYqlIq8eWlkjdcq2Iu8ClC/nExpuXRmmaFUBIBQKvLmpZE1OqvdvArQoIk3rcVCltHDhVYB5dWCLySLlAvUR6Uyo1YCL3Guv1dBE6/9PrXW7B6da/NbclNFYQWAlJAThOZR1UAowq5JOfFac4XT7mGv/JvNBOu1IM3SLlVYFVDekjIJgokUWWWmmmCplWKfO/zUTVnapQq7AxB3P0GIh6KqzFS7G5XRu5m5w29BasefZKGuLqwAEHc/QRD86Nnag5mnZzC5bxK8yPW0INNHpmOfO4IWpFkJ2cKqgMTdTxAEP6ojVUwNT9VjG3iRMTU8he5N3bHPHaZmCS2sACiy7lIQhOZRqWWmj0zHPneYuiAtrAoIKK7uUhCyoGhu1X5qmbjnDp26x1lQaAEgCEI8FNGtOm07oYkL0sKqgARBiI8iulWbqpZJE9kBCIIQSBHdqk1Vy6SJCABBEAIpqlu1iWqZNBEVkCAIgYi6pJjIDkAQhEBEXVJMRAAIgqBF2dUlRURUQIIgCCVFBIAgCEJJEQEgCIJQUkQACIIglBQRAIIgCCWFmDnrPighorcAvBrDpVYDeDuG6+SNMo67jGMGyjnuMo4Z0Bv3WmZeE3QhowVAXBDRBDP3Zd2PtCnjuMs4ZqCc4y7jmIF4xy0qIEEQhJIiAkAQBKGklEUA7Mu6AxlRxnGXccxAOcddxjEDMY67FDYAQRAEYTll2QEIgiAILgolAIjoWiJ6iYiOEtEdHu+/h4i+Zb3/LBGtS7+X8aIx5v9IRC8S0fNENE5Ea7PoZ9wEjdtx3KeIiIko994iOmMmoj+wnvcLRPSNtPuYBBr/411E9D0i+pH1f74pi37GCRE9RERvEtFPFO8TEd1n3ZPniejySB/EzIX4AdAK4B8B/AaADgBTAD7oOuZ2AA9Yf98I4FtZ9zuFMf8OgBXW37flfcy647aOey+AvwPwDIC+rPudwrPuBvAjAOdbry/Iut8pjXsfgNusvz8I4JWs+x3DuH8bwOUAfqJ4fxOAMQAE4CoAz0b5nCLtAK4AcJSZX2bmeQCPAdjiOmYLgGHr728D2EBElGIf4yZwzMz8PWY+ab18BsDFKfcxCXSeNQB8BcA9AP45zc4lhM6Y/xDAXzLzOwDAzG+m3Mck0Bk3AzjX+rsTwOsp9i8RmPnvAJzwOWQLgIe5xjMAziOiC8N+TpEEwEUAXnO8Pma1eR7DzKcBzAJYlUrvkkFnzE5uRW3VkHcCx01EHwFwCTN/J82OJYjOs/5NAL9JRE8T0TNEdG1qvUsOnXF/GcBNRHQMwBEAf5xO1zIl7HffkyIVhPFaybtdnHSOyRPa4yGimwD0Afi3ifYoHXzHTUQtAL4G4LNpdSgFdJ51G2pqoI+httP7eyL6MDP/IuG+JYnOuP8dgL9h5v9GRB8F8Ig17qXku5cZscxlRdoBHANwieP1xVi+FawfQ0RtqG0X/bZZpqMzZhDR7wHYBeCTzHwqpb4lSdC43wvgwwC+T0SvoKYjPZRzQ7Du//dBZl5g5p8BeAk1gZBndMZ9K4DHAYCZfwDgLNTy5RQZre9+EEUSAM8B6CaiS4moAzUj7yHXMYcADFh/fwrAd9myqOSUwDFbqpC/Qm3yL4JOGAgYNzPPMvNqZl7HzOtQs318kpknsuluLOj8fx9AzegPIlqNmkro5VR7GT86454BsAEAiOgDqAmAt1LtZfocAvAZyxvoKgCzzPxG2IsURgXEzKeJ6AsAnkTNc+AhZn6BiO4CMMHMhwA8iNr28ChqK/8bs+tx82iO+c8BnAPgf1r27hlm/mRmnY4BzXEXCs0xPwngGiJ6EcAigP/EzMez63XzaI77TwD8NRH9B9TUIJ/N+cIORPRN1FR5qy3bxhCAdgBg5gdQs3VsAnAUwEkAN0f6nJzfJ0EQBCEiRVIBCYIgCCEQASAIglBSRAAIgiCUFBEAgiAIJUUEgCAIQkkRASAIglBSRAAIgiCUFBEAgiAIJeX/AyO/I6aqBcDvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#湿度\n",
    "plt.scatter(data_train[\"hum\"].values,data_train[\"cnt\"].values,color='purple')#湿度较小时租车人数少\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#风速\n",
    "plt.scatter(data_train[\"windspeed\"].values,data_train[\"cnt\"].values,color='purple')#风速大时租车人数少\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#各特征间及与租车人数的相关性。数据中离散特征在计算相关性时不用特别处理\n",
    "data_corr = data_train.corr().abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从以上分析中，根据特征对租车人数进行回归拟合\n",
    "#atemp与temp相关性较大，选择atemp为温度特征\n",
    "#season,mnth，日期相关性较大，选season为时间特征\n",
    "#hum与weathersit有一定的相关性，但没有很大，选weathersit为天气特征，但定性来说湿度也有一定的影响，因此也考虑湿度特征\n",
    "#风速对租车人数也有一定的影响，也考虑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dteday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-02</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>6</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.204348</td>\n",
       "      <td>0.233209</td>\n",
       "      <td>0.518261</td>\n",
       "      <td>0.089565</td>\n",
       "      <td>1606</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>7</td>\n",
       "      <td>2011-01-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.196522</td>\n",
       "      <td>0.208839</td>\n",
       "      <td>0.498696</td>\n",
       "      <td>0.168726</td>\n",
       "      <td>1510</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-08</th>\n",
       "      <td>8</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.162254</td>\n",
       "      <td>0.535833</td>\n",
       "      <td>0.266804</td>\n",
       "      <td>959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-09</th>\n",
       "      <td>9</td>\n",
       "      <td>2011-01-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.138333</td>\n",
       "      <td>0.116175</td>\n",
       "      <td>0.434167</td>\n",
       "      <td>0.361950</td>\n",
       "      <td>822</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>10</td>\n",
       "      <td>2011-01-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150833</td>\n",
       "      <td>0.150888</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>0.223267</td>\n",
       "      <td>1321</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-11</th>\n",
       "      <td>11</td>\n",
       "      <td>2011-01-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.169091</td>\n",
       "      <td>0.191464</td>\n",
       "      <td>0.686364</td>\n",
       "      <td>0.122132</td>\n",
       "      <td>1263</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-12</th>\n",
       "      <td>12</td>\n",
       "      <td>2011-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.172727</td>\n",
       "      <td>0.160473</td>\n",
       "      <td>0.599545</td>\n",
       "      <td>0.304627</td>\n",
       "      <td>1162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-13</th>\n",
       "      <td>13</td>\n",
       "      <td>2011-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.150883</td>\n",
       "      <td>0.470417</td>\n",
       "      <td>0.301000</td>\n",
       "      <td>1406</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-14</th>\n",
       "      <td>14</td>\n",
       "      <td>2011-01-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.160870</td>\n",
       "      <td>0.188413</td>\n",
       "      <td>0.537826</td>\n",
       "      <td>0.126548</td>\n",
       "      <td>1421</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-15</th>\n",
       "      <td>15</td>\n",
       "      <td>2011-01-15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.248112</td>\n",
       "      <td>0.498750</td>\n",
       "      <td>0.157963</td>\n",
       "      <td>1248</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-16</th>\n",
       "      <td>16</td>\n",
       "      <td>2011-01-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.231667</td>\n",
       "      <td>0.234217</td>\n",
       "      <td>0.483750</td>\n",
       "      <td>0.188433</td>\n",
       "      <td>1204</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-17</th>\n",
       "      <td>17</td>\n",
       "      <td>2011-01-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.175833</td>\n",
       "      <td>0.176771</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>0.194017</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-18</th>\n",
       "      <td>18</td>\n",
       "      <td>2011-01-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>0.232333</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.146775</td>\n",
       "      <td>683</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-19</th>\n",
       "      <td>19</td>\n",
       "      <td>2011-01-19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.292174</td>\n",
       "      <td>0.298422</td>\n",
       "      <td>0.741739</td>\n",
       "      <td>0.208317</td>\n",
       "      <td>1650</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-20</th>\n",
       "      <td>20</td>\n",
       "      <td>2011-01-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.261667</td>\n",
       "      <td>0.255050</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.195904</td>\n",
       "      <td>1927</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-21</th>\n",
       "      <td>21</td>\n",
       "      <td>2011-01-21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.177500</td>\n",
       "      <td>0.157833</td>\n",
       "      <td>0.457083</td>\n",
       "      <td>0.353242</td>\n",
       "      <td>1543</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-22</th>\n",
       "      <td>22</td>\n",
       "      <td>2011-01-22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.171970</td>\n",
       "      <td>981</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-23</th>\n",
       "      <td>23</td>\n",
       "      <td>2011-01-23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.096522</td>\n",
       "      <td>0.098839</td>\n",
       "      <td>0.436522</td>\n",
       "      <td>0.246600</td>\n",
       "      <td>986</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-24</th>\n",
       "      <td>24</td>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.097391</td>\n",
       "      <td>0.117930</td>\n",
       "      <td>0.491739</td>\n",
       "      <td>0.158330</td>\n",
       "      <td>1416</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-25</th>\n",
       "      <td>25</td>\n",
       "      <td>2011-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.223478</td>\n",
       "      <td>0.234526</td>\n",
       "      <td>0.616957</td>\n",
       "      <td>0.129796</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-26</th>\n",
       "      <td>26</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.217500</td>\n",
       "      <td>0.203600</td>\n",
       "      <td>0.862500</td>\n",
       "      <td>0.293850</td>\n",
       "      <td>506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-27</th>\n",
       "      <td>27</td>\n",
       "      <td>2011-01-27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.195000</td>\n",
       "      <td>0.219700</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.113837</td>\n",
       "      <td>431</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-28</th>\n",
       "      <td>28</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.203478</td>\n",
       "      <td>0.223317</td>\n",
       "      <td>0.793043</td>\n",
       "      <td>0.123300</td>\n",
       "      <td>1167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-29</th>\n",
       "      <td>29</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196522</td>\n",
       "      <td>0.212126</td>\n",
       "      <td>0.651739</td>\n",
       "      <td>0.145365</td>\n",
       "      <td>1098</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-30</th>\n",
       "      <td>30</td>\n",
       "      <td>2011-01-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.216522</td>\n",
       "      <td>0.250322</td>\n",
       "      <td>0.722174</td>\n",
       "      <td>0.073983</td>\n",
       "      <td>1096</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-02</th>\n",
       "      <td>336</td>\n",
       "      <td>2011-12-02</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.314167</td>\n",
       "      <td>0.331433</td>\n",
       "      <td>0.625833</td>\n",
       "      <td>0.100754</td>\n",
       "      <td>3940</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-03</th>\n",
       "      <td>337</td>\n",
       "      <td>2011-12-03</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.299167</td>\n",
       "      <td>0.310604</td>\n",
       "      <td>0.612917</td>\n",
       "      <td>0.095783</td>\n",
       "      <td>3614</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-04</th>\n",
       "      <td>338</td>\n",
       "      <td>2011-12-04</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.330833</td>\n",
       "      <td>0.349100</td>\n",
       "      <td>0.775833</td>\n",
       "      <td>0.083958</td>\n",
       "      <td>3485</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-05</th>\n",
       "      <td>339</td>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.385833</td>\n",
       "      <td>0.393925</td>\n",
       "      <td>0.827083</td>\n",
       "      <td>0.062208</td>\n",
       "      <td>3811</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-06</th>\n",
       "      <td>340</td>\n",
       "      <td>2011-12-06</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.462500</td>\n",
       "      <td>0.456400</td>\n",
       "      <td>0.949583</td>\n",
       "      <td>0.232583</td>\n",
       "      <td>2594</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-07</th>\n",
       "      <td>341</td>\n",
       "      <td>2011-12-07</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.400246</td>\n",
       "      <td>0.970417</td>\n",
       "      <td>0.266175</td>\n",
       "      <td>705</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-08</th>\n",
       "      <td>342</td>\n",
       "      <td>2011-12-08</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265833</td>\n",
       "      <td>0.256938</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.240058</td>\n",
       "      <td>3322</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-09</th>\n",
       "      <td>343</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.290833</td>\n",
       "      <td>0.317542</td>\n",
       "      <td>0.695833</td>\n",
       "      <td>0.082717</td>\n",
       "      <td>3620</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-10</th>\n",
       "      <td>344</td>\n",
       "      <td>2011-12-10</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.266412</td>\n",
       "      <td>0.507500</td>\n",
       "      <td>0.233221</td>\n",
       "      <td>3190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-11</th>\n",
       "      <td>345</td>\n",
       "      <td>2011-12-11</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.220833</td>\n",
       "      <td>0.253154</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.066542</td>\n",
       "      <td>2743</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-12</th>\n",
       "      <td>346</td>\n",
       "      <td>2011-12-12</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.238333</td>\n",
       "      <td>0.270196</td>\n",
       "      <td>0.670833</td>\n",
       "      <td>0.063450</td>\n",
       "      <td>3310</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-13</th>\n",
       "      <td>347</td>\n",
       "      <td>2011-12-13</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.282500</td>\n",
       "      <td>0.301138</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.140550</td>\n",
       "      <td>3523</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-14</th>\n",
       "      <td>348</td>\n",
       "      <td>2011-12-14</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.317500</td>\n",
       "      <td>0.338362</td>\n",
       "      <td>0.663750</td>\n",
       "      <td>0.060958</td>\n",
       "      <td>3740</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-15</th>\n",
       "      <td>349</td>\n",
       "      <td>2011-12-15</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.422500</td>\n",
       "      <td>0.412237</td>\n",
       "      <td>0.634167</td>\n",
       "      <td>0.268042</td>\n",
       "      <td>3709</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-16</th>\n",
       "      <td>350</td>\n",
       "      <td>2011-12-16</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.359825</td>\n",
       "      <td>0.500417</td>\n",
       "      <td>0.260575</td>\n",
       "      <td>3577</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-17</th>\n",
       "      <td>351</td>\n",
       "      <td>2011-12-17</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.258333</td>\n",
       "      <td>0.249371</td>\n",
       "      <td>0.560833</td>\n",
       "      <td>0.243167</td>\n",
       "      <td>2739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-18</th>\n",
       "      <td>352</td>\n",
       "      <td>2011-12-18</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.238333</td>\n",
       "      <td>0.245579</td>\n",
       "      <td>0.586250</td>\n",
       "      <td>0.169779</td>\n",
       "      <td>2431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-19</th>\n",
       "      <td>353</td>\n",
       "      <td>2011-12-19</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.276667</td>\n",
       "      <td>0.280933</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>0.172896</td>\n",
       "      <td>3403</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-20</th>\n",
       "      <td>354</td>\n",
       "      <td>2011-12-20</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.385833</td>\n",
       "      <td>0.396454</td>\n",
       "      <td>0.595417</td>\n",
       "      <td>0.061571</td>\n",
       "      <td>3750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-21</th>\n",
       "      <td>355</td>\n",
       "      <td>2011-12-21</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.428333</td>\n",
       "      <td>0.428017</td>\n",
       "      <td>0.858333</td>\n",
       "      <td>0.221400</td>\n",
       "      <td>2660</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-22</th>\n",
       "      <td>356</td>\n",
       "      <td>2011-12-22</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.423333</td>\n",
       "      <td>0.426121</td>\n",
       "      <td>0.757500</td>\n",
       "      <td>0.047275</td>\n",
       "      <td>3068</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-23</th>\n",
       "      <td>357</td>\n",
       "      <td>2011-12-23</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.373333</td>\n",
       "      <td>0.377513</td>\n",
       "      <td>0.686250</td>\n",
       "      <td>0.274246</td>\n",
       "      <td>2209</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-24</th>\n",
       "      <td>358</td>\n",
       "      <td>2011-12-24</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.302500</td>\n",
       "      <td>0.299242</td>\n",
       "      <td>0.542500</td>\n",
       "      <td>0.190304</td>\n",
       "      <td>1011</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-25</th>\n",
       "      <td>359</td>\n",
       "      <td>2011-12-25</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.274783</td>\n",
       "      <td>0.279961</td>\n",
       "      <td>0.681304</td>\n",
       "      <td>0.155091</td>\n",
       "      <td>754</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-26</th>\n",
       "      <td>360</td>\n",
       "      <td>2011-12-26</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.321739</td>\n",
       "      <td>0.315535</td>\n",
       "      <td>0.506957</td>\n",
       "      <td>0.239465</td>\n",
       "      <td>1317</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-27</th>\n",
       "      <td>361</td>\n",
       "      <td>2011-12-27</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.327633</td>\n",
       "      <td>0.762500</td>\n",
       "      <td>0.188450</td>\n",
       "      <td>1162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-28</th>\n",
       "      <td>362</td>\n",
       "      <td>2011-12-28</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.299130</td>\n",
       "      <td>0.279974</td>\n",
       "      <td>0.503913</td>\n",
       "      <td>0.293961</td>\n",
       "      <td>2302</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-29</th>\n",
       "      <td>363</td>\n",
       "      <td>2011-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.248333</td>\n",
       "      <td>0.263892</td>\n",
       "      <td>0.574167</td>\n",
       "      <td>0.119412</td>\n",
       "      <td>2423</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-30</th>\n",
       "      <td>364</td>\n",
       "      <td>2011-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.311667</td>\n",
       "      <td>0.318812</td>\n",
       "      <td>0.636667</td>\n",
       "      <td>0.134337</td>\n",
       "      <td>2999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>365</td>\n",
       "      <td>2011-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.414121</td>\n",
       "      <td>0.615833</td>\n",
       "      <td>0.220154</td>\n",
       "      <td>2485</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            instant     dteday  season  mnth  holiday  weekday  workingday  \\\n",
       "dteday                                                                       \n",
       "2011-01-01        1 2011-01-01       1     1        0        6           0   \n",
       "2011-01-02        2 2011-01-02       1     1        0        0           0   \n",
       "2011-01-03        3 2011-01-03       1     1        0        1           1   \n",
       "2011-01-04        4 2011-01-04       1     1        0        2           1   \n",
       "2011-01-05        5 2011-01-05       1     1        0        3           1   \n",
       "2011-01-06        6 2011-01-06       1     1        0        4           1   \n",
       "2011-01-07        7 2011-01-07       1     1        0        5           1   \n",
       "2011-01-08        8 2011-01-08       1     1        0        6           0   \n",
       "2011-01-09        9 2011-01-09       1     1        0        0           0   \n",
       "2011-01-10       10 2011-01-10       1     1        0        1           1   \n",
       "2011-01-11       11 2011-01-11       1     1        0        2           1   \n",
       "2011-01-12       12 2011-01-12       1     1        0        3           1   \n",
       "2011-01-13       13 2011-01-13       1     1        0        4           1   \n",
       "2011-01-14       14 2011-01-14       1     1        0        5           1   \n",
       "2011-01-15       15 2011-01-15       1     1        0        6           0   \n",
       "2011-01-16       16 2011-01-16       1     1        0        0           0   \n",
       "2011-01-17       17 2011-01-17       1     1        1        1           0   \n",
       "2011-01-18       18 2011-01-18       1     1        0        2           1   \n",
       "2011-01-19       19 2011-01-19       1     1        0        3           1   \n",
       "2011-01-20       20 2011-01-20       1     1        0        4           1   \n",
       "2011-01-21       21 2011-01-21       1     1        0        5           1   \n",
       "2011-01-22       22 2011-01-22       1     1        0        6           0   \n",
       "2011-01-23       23 2011-01-23       1     1        0        0           0   \n",
       "2011-01-24       24 2011-01-24       1     1        0        1           1   \n",
       "2011-01-25       25 2011-01-25       1     1        0        2           1   \n",
       "2011-01-26       26 2011-01-26       1     1        0        3           1   \n",
       "2011-01-27       27 2011-01-27       1     1        0        4           1   \n",
       "2011-01-28       28 2011-01-28       1     1        0        5           1   \n",
       "2011-01-29       29 2011-01-29       1     1        0        6           0   \n",
       "2011-01-30       30 2011-01-30       1     1        0        0           0   \n",
       "...             ...        ...     ...   ...      ...      ...         ...   \n",
       "2011-12-02      336 2011-12-02       4    12        0        5           1   \n",
       "2011-12-03      337 2011-12-03       4    12        0        6           0   \n",
       "2011-12-04      338 2011-12-04       4    12        0        0           0   \n",
       "2011-12-05      339 2011-12-05       4    12        0        1           1   \n",
       "2011-12-06      340 2011-12-06       4    12        0        2           1   \n",
       "2011-12-07      341 2011-12-07       4    12        0        3           1   \n",
       "2011-12-08      342 2011-12-08       4    12        0        4           1   \n",
       "2011-12-09      343 2011-12-09       4    12        0        5           1   \n",
       "2011-12-10      344 2011-12-10       4    12        0        6           0   \n",
       "2011-12-11      345 2011-12-11       4    12        0        0           0   \n",
       "2011-12-12      346 2011-12-12       4    12        0        1           1   \n",
       "2011-12-13      347 2011-12-13       4    12        0        2           1   \n",
       "2011-12-14      348 2011-12-14       4    12        0        3           1   \n",
       "2011-12-15      349 2011-12-15       4    12        0        4           1   \n",
       "2011-12-16      350 2011-12-16       4    12        0        5           1   \n",
       "2011-12-17      351 2011-12-17       4    12        0        6           0   \n",
       "2011-12-18      352 2011-12-18       4    12        0        0           0   \n",
       "2011-12-19      353 2011-12-19       4    12        0        1           1   \n",
       "2011-12-20      354 2011-12-20       4    12        0        2           1   \n",
       "2011-12-21      355 2011-12-21       1    12        0        3           1   \n",
       "2011-12-22      356 2011-12-22       1    12        0        4           1   \n",
       "2011-12-23      357 2011-12-23       1    12        0        5           1   \n",
       "2011-12-24      358 2011-12-24       1    12        0        6           0   \n",
       "2011-12-25      359 2011-12-25       1    12        0        0           0   \n",
       "2011-12-26      360 2011-12-26       1    12        1        1           0   \n",
       "2011-12-27      361 2011-12-27       1    12        0        2           1   \n",
       "2011-12-28      362 2011-12-28       1    12        0        3           1   \n",
       "2011-12-29      363 2011-12-29       1    12        0        4           1   \n",
       "2011-12-30      364 2011-12-30       1    12        0        5           1   \n",
       "2011-12-31      365 2011-12-31       1    12        0        6           0   \n",
       "\n",
       "            weathersit      temp     atemp       hum  windspeed   cnt  \\\n",
       "dteday                                                                  \n",
       "2011-01-01           2  0.344167  0.363625  0.805833   0.160446   985   \n",
       "2011-01-02           2  0.363478  0.353739  0.696087   0.248539   801   \n",
       "2011-01-03           1  0.196364  0.189405  0.437273   0.248309  1349   \n",
       "2011-01-04           1  0.200000  0.212122  0.590435   0.160296  1562   \n",
       "2011-01-05           1  0.226957  0.229270  0.436957   0.186900  1600   \n",
       "2011-01-06           1  0.204348  0.233209  0.518261   0.089565  1606   \n",
       "2011-01-07           2  0.196522  0.208839  0.498696   0.168726  1510   \n",
       "2011-01-08           2  0.165000  0.162254  0.535833   0.266804   959   \n",
       "2011-01-09           1  0.138333  0.116175  0.434167   0.361950   822   \n",
       "2011-01-10           1  0.150833  0.150888  0.482917   0.223267  1321   \n",
       "2011-01-11           2  0.169091  0.191464  0.686364   0.122132  1263   \n",
       "2011-01-12           1  0.172727  0.160473  0.599545   0.304627  1162   \n",
       "2011-01-13           1  0.165000  0.150883  0.470417   0.301000  1406   \n",
       "2011-01-14           1  0.160870  0.188413  0.537826   0.126548  1421   \n",
       "2011-01-15           2  0.233333  0.248112  0.498750   0.157963  1248   \n",
       "2011-01-16           1  0.231667  0.234217  0.483750   0.188433  1204   \n",
       "2011-01-17           2  0.175833  0.176771  0.537500   0.194017  1000   \n",
       "2011-01-18           2  0.216667  0.232333  0.861667   0.146775   683   \n",
       "2011-01-19           2  0.292174  0.298422  0.741739   0.208317  1650   \n",
       "2011-01-20           2  0.261667  0.255050  0.538333   0.195904  1927   \n",
       "2011-01-21           1  0.177500  0.157833  0.457083   0.353242  1543   \n",
       "2011-01-22           1  0.059130  0.079070  0.400000   0.171970   981   \n",
       "2011-01-23           1  0.096522  0.098839  0.436522   0.246600   986   \n",
       "2011-01-24           1  0.097391  0.117930  0.491739   0.158330  1416   \n",
       "2011-01-25           2  0.223478  0.234526  0.616957   0.129796  1985   \n",
       "2011-01-26           3  0.217500  0.203600  0.862500   0.293850   506   \n",
       "2011-01-27           1  0.195000  0.219700  0.687500   0.113837   431   \n",
       "2011-01-28           2  0.203478  0.223317  0.793043   0.123300  1167   \n",
       "2011-01-29           1  0.196522  0.212126  0.651739   0.145365  1098   \n",
       "2011-01-30           1  0.216522  0.250322  0.722174   0.073983  1096   \n",
       "...                ...       ...       ...       ...        ...   ...   \n",
       "2011-12-02           1  0.314167  0.331433  0.625833   0.100754  3940   \n",
       "2011-12-03           1  0.299167  0.310604  0.612917   0.095783  3614   \n",
       "2011-12-04           1  0.330833  0.349100  0.775833   0.083958  3485   \n",
       "2011-12-05           2  0.385833  0.393925  0.827083   0.062208  3811   \n",
       "2011-12-06           3  0.462500  0.456400  0.949583   0.232583  2594   \n",
       "2011-12-07           3  0.410000  0.400246  0.970417   0.266175   705   \n",
       "2011-12-08           1  0.265833  0.256938  0.580000   0.240058  3322   \n",
       "2011-12-09           1  0.290833  0.317542  0.695833   0.082717  3620   \n",
       "2011-12-10           1  0.275000  0.266412  0.507500   0.233221  3190   \n",
       "2011-12-11           1  0.220833  0.253154  0.490000   0.066542  2743   \n",
       "2011-12-12           1  0.238333  0.270196  0.670833   0.063450  3310   \n",
       "2011-12-13           1  0.282500  0.301138  0.590000   0.140550  3523   \n",
       "2011-12-14           2  0.317500  0.338362  0.663750   0.060958  3740   \n",
       "2011-12-15           2  0.422500  0.412237  0.634167   0.268042  3709   \n",
       "2011-12-16           2  0.375000  0.359825  0.500417   0.260575  3577   \n",
       "2011-12-17           2  0.258333  0.249371  0.560833   0.243167  2739   \n",
       "2011-12-18           1  0.238333  0.245579  0.586250   0.169779  2431   \n",
       "2011-12-19           1  0.276667  0.280933  0.637500   0.172896  3403   \n",
       "2011-12-20           2  0.385833  0.396454  0.595417   0.061571  3750   \n",
       "2011-12-21           2  0.428333  0.428017  0.858333   0.221400  2660   \n",
       "2011-12-22           2  0.423333  0.426121  0.757500   0.047275  3068   \n",
       "2011-12-23           1  0.373333  0.377513  0.686250   0.274246  2209   \n",
       "2011-12-24           1  0.302500  0.299242  0.542500   0.190304  1011   \n",
       "2011-12-25           1  0.274783  0.279961  0.681304   0.155091   754   \n",
       "2011-12-26           1  0.321739  0.315535  0.506957   0.239465  1317   \n",
       "2011-12-27           2  0.325000  0.327633  0.762500   0.188450  1162   \n",
       "2011-12-28           1  0.299130  0.279974  0.503913   0.293961  2302   \n",
       "2011-12-29           1  0.248333  0.263892  0.574167   0.119412  2423   \n",
       "2011-12-30           1  0.311667  0.318812  0.636667   0.134337  2999   \n",
       "2011-12-31           1  0.410000  0.414121  0.615833   0.220154  2485   \n",
       "\n",
       "            season_1  season_2  season_3  season_4  weathersit_1  \\\n",
       "dteday                                                             \n",
       "2011-01-01         1         0         0         0             0   \n",
       "2011-01-02         1         0         0         0             0   \n",
       "2011-01-03         1         0         0         0             1   \n",
       "2011-01-04         1         0         0         0             1   \n",
       "2011-01-05         1         0         0         0             1   \n",
       "2011-01-06         1         0         0         0             1   \n",
       "2011-01-07         1         0         0         0             0   \n",
       "2011-01-08         1         0         0         0             0   \n",
       "2011-01-09         1         0         0         0             1   \n",
       "2011-01-10         1         0         0         0             1   \n",
       "2011-01-11         1         0         0         0             0   \n",
       "2011-01-12         1         0         0         0             1   \n",
       "2011-01-13         1         0         0         0             1   \n",
       "2011-01-14         1         0         0         0             1   \n",
       "2011-01-15         1         0         0         0             0   \n",
       "2011-01-16         1         0         0         0             1   \n",
       "2011-01-17         1         0         0         0             0   \n",
       "2011-01-18         1         0         0         0             0   \n",
       "2011-01-19         1         0         0         0             0   \n",
       "2011-01-20         1         0         0         0             0   \n",
       "2011-01-21         1         0         0         0             1   \n",
       "2011-01-22         1         0         0         0             1   \n",
       "2011-01-23         1         0         0         0             1   \n",
       "2011-01-24         1         0         0         0             1   \n",
       "2011-01-25         1         0         0         0             0   \n",
       "2011-01-26         1         0         0         0             0   \n",
       "2011-01-27         1         0         0         0             1   \n",
       "2011-01-28         1         0         0         0             0   \n",
       "2011-01-29         1         0         0         0             1   \n",
       "2011-01-30         1         0         0         0             1   \n",
       "...              ...       ...       ...       ...           ...   \n",
       "2011-12-02         0         0         0         1             1   \n",
       "2011-12-03         0         0         0         1             1   \n",
       "2011-12-04         0         0         0         1             1   \n",
       "2011-12-05         0         0         0         1             0   \n",
       "2011-12-06         0         0         0         1             0   \n",
       "2011-12-07         0         0         0         1             0   \n",
       "2011-12-08         0         0         0         1             1   \n",
       "2011-12-09         0         0         0         1             1   \n",
       "2011-12-10         0         0         0         1             1   \n",
       "2011-12-11         0         0         0         1             1   \n",
       "2011-12-12         0         0         0         1             1   \n",
       "2011-12-13         0         0         0         1             1   \n",
       "2011-12-14         0         0         0         1             0   \n",
       "2011-12-15         0         0         0         1             0   \n",
       "2011-12-16         0         0         0         1             0   \n",
       "2011-12-17         0         0         0         1             0   \n",
       "2011-12-18         0         0         0         1             1   \n",
       "2011-12-19         0         0         0         1             1   \n",
       "2011-12-20         0         0         0         1             0   \n",
       "2011-12-21         1         0         0         0             0   \n",
       "2011-12-22         1         0         0         0             0   \n",
       "2011-12-23         1         0         0         0             1   \n",
       "2011-12-24         1         0         0         0             1   \n",
       "2011-12-25         1         0         0         0             1   \n",
       "2011-12-26         1         0         0         0             1   \n",
       "2011-12-27         1         0         0         0             0   \n",
       "2011-12-28         1         0         0         0             1   \n",
       "2011-12-29         1         0         0         0             1   \n",
       "2011-12-30         1         0         0         0             1   \n",
       "2011-12-31         1         0         0         0             1   \n",
       "\n",
       "            weathersit_2  weathersit_3  \n",
       "dteday                                  \n",
       "2011-01-01             1             0  \n",
       "2011-01-02             1             0  \n",
       "2011-01-03             0             0  \n",
       "2011-01-04             0             0  \n",
       "2011-01-05             0             0  \n",
       "2011-01-06             0             0  \n",
       "2011-01-07             1             0  \n",
       "2011-01-08             1             0  \n",
       "2011-01-09             0             0  \n",
       "2011-01-10             0             0  \n",
       "2011-01-11             1             0  \n",
       "2011-01-12             0             0  \n",
       "2011-01-13             0             0  \n",
       "2011-01-14             0             0  \n",
       "2011-01-15             1             0  \n",
       "2011-01-16             0             0  \n",
       "2011-01-17             1             0  \n",
       "2011-01-18             1             0  \n",
       "2011-01-19             1             0  \n",
       "2011-01-20             1             0  \n",
       "2011-01-21             0             0  \n",
       "2011-01-22             0             0  \n",
       "2011-01-23             0             0  \n",
       "2011-01-24             0             0  \n",
       "2011-01-25             1             0  \n",
       "2011-01-26             0             1  \n",
       "2011-01-27             0             0  \n",
       "2011-01-28             1             0  \n",
       "2011-01-29             0             0  \n",
       "2011-01-30             0             0  \n",
       "...                  ...           ...  \n",
       "2011-12-02             0             0  \n",
       "2011-12-03             0             0  \n",
       "2011-12-04             0             0  \n",
       "2011-12-05             1             0  \n",
       "2011-12-06             0             1  \n",
       "2011-12-07             0             1  \n",
       "2011-12-08             0             0  \n",
       "2011-12-09             0             0  \n",
       "2011-12-10             0             0  \n",
       "2011-12-11             0             0  \n",
       "2011-12-12             0             0  \n",
       "2011-12-13             0             0  \n",
       "2011-12-14             1             0  \n",
       "2011-12-15             1             0  \n",
       "2011-12-16             1             0  \n",
       "2011-12-17             1             0  \n",
       "2011-12-18             0             0  \n",
       "2011-12-19             0             0  \n",
       "2011-12-20             1             0  \n",
       "2011-12-21             1             0  \n",
       "2011-12-22             1             0  \n",
       "2011-12-23             0             0  \n",
       "2011-12-24             0             0  \n",
       "2011-12-25             0             0  \n",
       "2011-12-26             0             0  \n",
       "2011-12-27             1             0  \n",
       "2011-12-28             0             0  \n",
       "2011-12-29             0             0  \n",
       "2011-12-30             0             0  \n",
       "2011-12-31             0             0  \n",
       "\n",
       "[365 rows x 20 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对训练集类别变量进行处理\n",
    "season0=pd.get_dummies(data_train[\"season\"],prefix=\"season\")\n",
    "data_train_d=data_train.join(season0)\n",
    "weathersit0=pd.get_dummies(data_train[\"weathersit\"],prefix=\"weathersit\")\n",
    "data_train_d=data_train_d.join(weathersit0)\n",
    "data_train_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dteday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>366</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.375621</td>\n",
       "      <td>0.692500</td>\n",
       "      <td>0.192167</td>\n",
       "      <td>2294</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>367</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.273043</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>0.381304</td>\n",
       "      <td>0.329665</td>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>368</td>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.126275</td>\n",
       "      <td>0.441250</td>\n",
       "      <td>0.365671</td>\n",
       "      <td>2236</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>369</td>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.107500</td>\n",
       "      <td>0.119337</td>\n",
       "      <td>0.414583</td>\n",
       "      <td>0.184700</td>\n",
       "      <td>2368</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>370</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265833</td>\n",
       "      <td>0.278412</td>\n",
       "      <td>0.524167</td>\n",
       "      <td>0.129987</td>\n",
       "      <td>3272</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>371</td>\n",
       "      <td>2012-01-06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.334167</td>\n",
       "      <td>0.340267</td>\n",
       "      <td>0.542083</td>\n",
       "      <td>0.167908</td>\n",
       "      <td>4098</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-07</th>\n",
       "      <td>372</td>\n",
       "      <td>2012-01-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.393333</td>\n",
       "      <td>0.390779</td>\n",
       "      <td>0.531667</td>\n",
       "      <td>0.174758</td>\n",
       "      <td>4521</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-08</th>\n",
       "      <td>373</td>\n",
       "      <td>2012-01-08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.337500</td>\n",
       "      <td>0.340258</td>\n",
       "      <td>0.465000</td>\n",
       "      <td>0.191542</td>\n",
       "      <td>3425</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>374</td>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.224167</td>\n",
       "      <td>0.247479</td>\n",
       "      <td>0.701667</td>\n",
       "      <td>0.098900</td>\n",
       "      <td>2376</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10</th>\n",
       "      <td>375</td>\n",
       "      <td>2012-01-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.308696</td>\n",
       "      <td>0.318826</td>\n",
       "      <td>0.646522</td>\n",
       "      <td>0.187552</td>\n",
       "      <td>3598</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-11</th>\n",
       "      <td>376</td>\n",
       "      <td>2012-01-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.274167</td>\n",
       "      <td>0.282821</td>\n",
       "      <td>0.847500</td>\n",
       "      <td>0.131221</td>\n",
       "      <td>2177</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-12</th>\n",
       "      <td>377</td>\n",
       "      <td>2012-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.382500</td>\n",
       "      <td>0.381938</td>\n",
       "      <td>0.802917</td>\n",
       "      <td>0.180967</td>\n",
       "      <td>4097</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-13</th>\n",
       "      <td>378</td>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.274167</td>\n",
       "      <td>0.249362</td>\n",
       "      <td>0.507500</td>\n",
       "      <td>0.378108</td>\n",
       "      <td>3214</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-14</th>\n",
       "      <td>379</td>\n",
       "      <td>2012-01-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.183087</td>\n",
       "      <td>0.457500</td>\n",
       "      <td>0.187183</td>\n",
       "      <td>2493</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-15</th>\n",
       "      <td>380</td>\n",
       "      <td>2012-01-15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.161625</td>\n",
       "      <td>0.419167</td>\n",
       "      <td>0.251258</td>\n",
       "      <td>2311</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-16</th>\n",
       "      <td>381</td>\n",
       "      <td>2012-01-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.190663</td>\n",
       "      <td>0.522500</td>\n",
       "      <td>0.231358</td>\n",
       "      <td>2298</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-17</th>\n",
       "      <td>382</td>\n",
       "      <td>2012-01-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.373043</td>\n",
       "      <td>0.364278</td>\n",
       "      <td>0.716087</td>\n",
       "      <td>0.349130</td>\n",
       "      <td>2935</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-18</th>\n",
       "      <td>383</td>\n",
       "      <td>2012-01-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.303333</td>\n",
       "      <td>0.275254</td>\n",
       "      <td>0.443333</td>\n",
       "      <td>0.415429</td>\n",
       "      <td>3376</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-19</th>\n",
       "      <td>384</td>\n",
       "      <td>2012-01-19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.190038</td>\n",
       "      <td>0.497500</td>\n",
       "      <td>0.220158</td>\n",
       "      <td>3292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-20</th>\n",
       "      <td>385</td>\n",
       "      <td>2012-01-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.217500</td>\n",
       "      <td>0.220958</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.202750</td>\n",
       "      <td>3163</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-21</th>\n",
       "      <td>386</td>\n",
       "      <td>2012-01-21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.173333</td>\n",
       "      <td>0.174875</td>\n",
       "      <td>0.831250</td>\n",
       "      <td>0.222642</td>\n",
       "      <td>1301</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-22</th>\n",
       "      <td>387</td>\n",
       "      <td>2012-01-22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.162500</td>\n",
       "      <td>0.162250</td>\n",
       "      <td>0.796250</td>\n",
       "      <td>0.199638</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-23</th>\n",
       "      <td>388</td>\n",
       "      <td>2012-01-23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.218333</td>\n",
       "      <td>0.243058</td>\n",
       "      <td>0.911250</td>\n",
       "      <td>0.110708</td>\n",
       "      <td>2432</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-24</th>\n",
       "      <td>389</td>\n",
       "      <td>2012-01-24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.342500</td>\n",
       "      <td>0.349108</td>\n",
       "      <td>0.835833</td>\n",
       "      <td>0.123767</td>\n",
       "      <td>4339</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-25</th>\n",
       "      <td>390</td>\n",
       "      <td>2012-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.294167</td>\n",
       "      <td>0.294821</td>\n",
       "      <td>0.643750</td>\n",
       "      <td>0.161071</td>\n",
       "      <td>4270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-26</th>\n",
       "      <td>391</td>\n",
       "      <td>2012-01-26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.341667</td>\n",
       "      <td>0.356050</td>\n",
       "      <td>0.769583</td>\n",
       "      <td>0.073396</td>\n",
       "      <td>4075</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-27</th>\n",
       "      <td>392</td>\n",
       "      <td>2012-01-27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.415383</td>\n",
       "      <td>0.741250</td>\n",
       "      <td>0.342667</td>\n",
       "      <td>3456</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-28</th>\n",
       "      <td>393</td>\n",
       "      <td>2012-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.315833</td>\n",
       "      <td>0.326379</td>\n",
       "      <td>0.543333</td>\n",
       "      <td>0.210829</td>\n",
       "      <td>4023</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-29</th>\n",
       "      <td>394</td>\n",
       "      <td>2012-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.282500</td>\n",
       "      <td>0.272721</td>\n",
       "      <td>0.311250</td>\n",
       "      <td>0.240050</td>\n",
       "      <td>3243</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-30</th>\n",
       "      <td>395</td>\n",
       "      <td>2012-01-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.269167</td>\n",
       "      <td>0.262625</td>\n",
       "      <td>0.400833</td>\n",
       "      <td>0.215792</td>\n",
       "      <td>3624</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-02</th>\n",
       "      <td>702</td>\n",
       "      <td>2012-12-02</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.347500</td>\n",
       "      <td>0.359208</td>\n",
       "      <td>0.823333</td>\n",
       "      <td>0.124379</td>\n",
       "      <td>4649</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-03</th>\n",
       "      <td>703</td>\n",
       "      <td>2012-12-03</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.452500</td>\n",
       "      <td>0.455796</td>\n",
       "      <td>0.767500</td>\n",
       "      <td>0.082721</td>\n",
       "      <td>6234</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-04</th>\n",
       "      <td>704</td>\n",
       "      <td>2012-12-04</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.475833</td>\n",
       "      <td>0.469054</td>\n",
       "      <td>0.733750</td>\n",
       "      <td>0.174129</td>\n",
       "      <td>6606</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-05</th>\n",
       "      <td>705</td>\n",
       "      <td>2012-12-05</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.438333</td>\n",
       "      <td>0.428012</td>\n",
       "      <td>0.485000</td>\n",
       "      <td>0.324021</td>\n",
       "      <td>5729</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-06</th>\n",
       "      <td>706</td>\n",
       "      <td>2012-12-06</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.258204</td>\n",
       "      <td>0.508750</td>\n",
       "      <td>0.174754</td>\n",
       "      <td>5375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-07</th>\n",
       "      <td>707</td>\n",
       "      <td>2012-12-07</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.320833</td>\n",
       "      <td>0.321958</td>\n",
       "      <td>0.764167</td>\n",
       "      <td>0.130600</td>\n",
       "      <td>5008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-08</th>\n",
       "      <td>708</td>\n",
       "      <td>2012-12-08</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.381667</td>\n",
       "      <td>0.389508</td>\n",
       "      <td>0.911250</td>\n",
       "      <td>0.101379</td>\n",
       "      <td>5582</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-09</th>\n",
       "      <td>709</td>\n",
       "      <td>2012-12-09</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.384167</td>\n",
       "      <td>0.390146</td>\n",
       "      <td>0.905417</td>\n",
       "      <td>0.157975</td>\n",
       "      <td>3228</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-10</th>\n",
       "      <td>710</td>\n",
       "      <td>2012-12-10</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.435833</td>\n",
       "      <td>0.435575</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.190308</td>\n",
       "      <td>5170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-11</th>\n",
       "      <td>711</td>\n",
       "      <td>2012-12-11</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.353333</td>\n",
       "      <td>0.338363</td>\n",
       "      <td>0.596667</td>\n",
       "      <td>0.296037</td>\n",
       "      <td>5501</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-12</th>\n",
       "      <td>712</td>\n",
       "      <td>2012-12-12</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.297500</td>\n",
       "      <td>0.297338</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.162937</td>\n",
       "      <td>5319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-13</th>\n",
       "      <td>713</td>\n",
       "      <td>2012-12-13</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.295833</td>\n",
       "      <td>0.294188</td>\n",
       "      <td>0.485833</td>\n",
       "      <td>0.174129</td>\n",
       "      <td>5532</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-14</th>\n",
       "      <td>714</td>\n",
       "      <td>2012-12-14</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.281667</td>\n",
       "      <td>0.294192</td>\n",
       "      <td>0.642917</td>\n",
       "      <td>0.131229</td>\n",
       "      <td>5611</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-15</th>\n",
       "      <td>715</td>\n",
       "      <td>2012-12-15</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.324167</td>\n",
       "      <td>0.338383</td>\n",
       "      <td>0.650417</td>\n",
       "      <td>0.106350</td>\n",
       "      <td>5047</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-16</th>\n",
       "      <td>716</td>\n",
       "      <td>2012-12-16</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.362500</td>\n",
       "      <td>0.369938</td>\n",
       "      <td>0.838750</td>\n",
       "      <td>0.100742</td>\n",
       "      <td>3786</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-17</th>\n",
       "      <td>717</td>\n",
       "      <td>2012-12-17</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.393333</td>\n",
       "      <td>0.401500</td>\n",
       "      <td>0.907083</td>\n",
       "      <td>0.098258</td>\n",
       "      <td>4585</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-18</th>\n",
       "      <td>718</td>\n",
       "      <td>2012-12-18</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.410833</td>\n",
       "      <td>0.409708</td>\n",
       "      <td>0.666250</td>\n",
       "      <td>0.221404</td>\n",
       "      <td>5557</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19</th>\n",
       "      <td>719</td>\n",
       "      <td>2012-12-19</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.332500</td>\n",
       "      <td>0.342162</td>\n",
       "      <td>0.625417</td>\n",
       "      <td>0.184092</td>\n",
       "      <td>5267</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-20</th>\n",
       "      <td>720</td>\n",
       "      <td>2012-12-20</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.335217</td>\n",
       "      <td>0.667917</td>\n",
       "      <td>0.132463</td>\n",
       "      <td>4128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-21</th>\n",
       "      <td>721</td>\n",
       "      <td>2012-12-21</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.326667</td>\n",
       "      <td>0.301767</td>\n",
       "      <td>0.556667</td>\n",
       "      <td>0.374383</td>\n",
       "      <td>3623</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-22</th>\n",
       "      <td>722</td>\n",
       "      <td>2012-12-22</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265833</td>\n",
       "      <td>0.236113</td>\n",
       "      <td>0.441250</td>\n",
       "      <td>0.407346</td>\n",
       "      <td>1749</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-23</th>\n",
       "      <td>723</td>\n",
       "      <td>2012-12-23</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.245833</td>\n",
       "      <td>0.259471</td>\n",
       "      <td>0.515417</td>\n",
       "      <td>0.133083</td>\n",
       "      <td>1787</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-24</th>\n",
       "      <td>724</td>\n",
       "      <td>2012-12-24</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.231304</td>\n",
       "      <td>0.258900</td>\n",
       "      <td>0.791304</td>\n",
       "      <td>0.077230</td>\n",
       "      <td>920</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-25</th>\n",
       "      <td>725</td>\n",
       "      <td>2012-12-25</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.291304</td>\n",
       "      <td>0.294465</td>\n",
       "      <td>0.734783</td>\n",
       "      <td>0.168726</td>\n",
       "      <td>1013</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-26</th>\n",
       "      <td>726</td>\n",
       "      <td>2012-12-26</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.243333</td>\n",
       "      <td>0.220333</td>\n",
       "      <td>0.823333</td>\n",
       "      <td>0.316546</td>\n",
       "      <td>441</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-27</th>\n",
       "      <td>727</td>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.226642</td>\n",
       "      <td>0.652917</td>\n",
       "      <td>0.350133</td>\n",
       "      <td>2114</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-28</th>\n",
       "      <td>728</td>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.255046</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.155471</td>\n",
       "      <td>3095</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-29</th>\n",
       "      <td>729</td>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.242400</td>\n",
       "      <td>0.752917</td>\n",
       "      <td>0.124383</td>\n",
       "      <td>1341</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-30</th>\n",
       "      <td>730</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>1796</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>2729</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            instant     dteday  season  mnth  holiday  weekday  workingday  \\\n",
       "dteday                                                                       \n",
       "2012-01-01      366 2012-01-01       1     1        0        0           0   \n",
       "2012-01-02      367 2012-01-02       1     1        1        1           0   \n",
       "2012-01-03      368 2012-01-03       1     1        0        2           1   \n",
       "2012-01-04      369 2012-01-04       1     1        0        3           1   \n",
       "2012-01-05      370 2012-01-05       1     1        0        4           1   \n",
       "2012-01-06      371 2012-01-06       1     1        0        5           1   \n",
       "2012-01-07      372 2012-01-07       1     1        0        6           0   \n",
       "2012-01-08      373 2012-01-08       1     1        0        0           0   \n",
       "2012-01-09      374 2012-01-09       1     1        0        1           1   \n",
       "2012-01-10      375 2012-01-10       1     1        0        2           1   \n",
       "2012-01-11      376 2012-01-11       1     1        0        3           1   \n",
       "2012-01-12      377 2012-01-12       1     1        0        4           1   \n",
       "2012-01-13      378 2012-01-13       1     1        0        5           1   \n",
       "2012-01-14      379 2012-01-14       1     1        0        6           0   \n",
       "2012-01-15      380 2012-01-15       1     1        0        0           0   \n",
       "2012-01-16      381 2012-01-16       1     1        1        1           0   \n",
       "2012-01-17      382 2012-01-17       1     1        0        2           1   \n",
       "2012-01-18      383 2012-01-18       1     1        0        3           1   \n",
       "2012-01-19      384 2012-01-19       1     1        0        4           1   \n",
       "2012-01-20      385 2012-01-20       1     1        0        5           1   \n",
       "2012-01-21      386 2012-01-21       1     1        0        6           0   \n",
       "2012-01-22      387 2012-01-22       1     1        0        0           0   \n",
       "2012-01-23      388 2012-01-23       1     1        0        1           1   \n",
       "2012-01-24      389 2012-01-24       1     1        0        2           1   \n",
       "2012-01-25      390 2012-01-25       1     1        0        3           1   \n",
       "2012-01-26      391 2012-01-26       1     1        0        4           1   \n",
       "2012-01-27      392 2012-01-27       1     1        0        5           1   \n",
       "2012-01-28      393 2012-01-28       1     1        0        6           0   \n",
       "2012-01-29      394 2012-01-29       1     1        0        0           0   \n",
       "2012-01-30      395 2012-01-30       1     1        0        1           1   \n",
       "...             ...        ...     ...   ...      ...      ...         ...   \n",
       "2012-12-02      702 2012-12-02       4    12        0        0           0   \n",
       "2012-12-03      703 2012-12-03       4    12        0        1           1   \n",
       "2012-12-04      704 2012-12-04       4    12        0        2           1   \n",
       "2012-12-05      705 2012-12-05       4    12        0        3           1   \n",
       "2012-12-06      706 2012-12-06       4    12        0        4           1   \n",
       "2012-12-07      707 2012-12-07       4    12        0        5           1   \n",
       "2012-12-08      708 2012-12-08       4    12        0        6           0   \n",
       "2012-12-09      709 2012-12-09       4    12        0        0           0   \n",
       "2012-12-10      710 2012-12-10       4    12        0        1           1   \n",
       "2012-12-11      711 2012-12-11       4    12        0        2           1   \n",
       "2012-12-12      712 2012-12-12       4    12        0        3           1   \n",
       "2012-12-13      713 2012-12-13       4    12        0        4           1   \n",
       "2012-12-14      714 2012-12-14       4    12        0        5           1   \n",
       "2012-12-15      715 2012-12-15       4    12        0        6           0   \n",
       "2012-12-16      716 2012-12-16       4    12        0        0           0   \n",
       "2012-12-17      717 2012-12-17       4    12        0        1           1   \n",
       "2012-12-18      718 2012-12-18       4    12        0        2           1   \n",
       "2012-12-19      719 2012-12-19       4    12        0        3           1   \n",
       "2012-12-20      720 2012-12-20       4    12        0        4           1   \n",
       "2012-12-21      721 2012-12-21       1    12        0        5           1   \n",
       "2012-12-22      722 2012-12-22       1    12        0        6           0   \n",
       "2012-12-23      723 2012-12-23       1    12        0        0           0   \n",
       "2012-12-24      724 2012-12-24       1    12        0        1           1   \n",
       "2012-12-25      725 2012-12-25       1    12        1        2           0   \n",
       "2012-12-26      726 2012-12-26       1    12        0        3           1   \n",
       "2012-12-27      727 2012-12-27       1    12        0        4           1   \n",
       "2012-12-28      728 2012-12-28       1    12        0        5           1   \n",
       "2012-12-29      729 2012-12-29       1    12        0        6           0   \n",
       "2012-12-30      730 2012-12-30       1    12        0        0           0   \n",
       "2012-12-31      731 2012-12-31       1    12        0        1           1   \n",
       "\n",
       "            weathersit      temp     atemp       hum  windspeed   cnt  \\\n",
       "dteday                                                                  \n",
       "2012-01-01           1  0.370000  0.375621  0.692500   0.192167  2294   \n",
       "2012-01-02           1  0.273043  0.252304  0.381304   0.329665  1951   \n",
       "2012-01-03           1  0.150000  0.126275  0.441250   0.365671  2236   \n",
       "2012-01-04           2  0.107500  0.119337  0.414583   0.184700  2368   \n",
       "2012-01-05           1  0.265833  0.278412  0.524167   0.129987  3272   \n",
       "2012-01-06           1  0.334167  0.340267  0.542083   0.167908  4098   \n",
       "2012-01-07           1  0.393333  0.390779  0.531667   0.174758  4521   \n",
       "2012-01-08           1  0.337500  0.340258  0.465000   0.191542  3425   \n",
       "2012-01-09           2  0.224167  0.247479  0.701667   0.098900  2376   \n",
       "2012-01-10           1  0.308696  0.318826  0.646522   0.187552  3598   \n",
       "2012-01-11           2  0.274167  0.282821  0.847500   0.131221  2177   \n",
       "2012-01-12           2  0.382500  0.381938  0.802917   0.180967  4097   \n",
       "2012-01-13           1  0.274167  0.249362  0.507500   0.378108  3214   \n",
       "2012-01-14           1  0.180000  0.183087  0.457500   0.187183  2493   \n",
       "2012-01-15           1  0.166667  0.161625  0.419167   0.251258  2311   \n",
       "2012-01-16           1  0.190000  0.190663  0.522500   0.231358  2298   \n",
       "2012-01-17           2  0.373043  0.364278  0.716087   0.349130  2935   \n",
       "2012-01-18           1  0.303333  0.275254  0.443333   0.415429  3376   \n",
       "2012-01-19           1  0.190000  0.190038  0.497500   0.220158  3292   \n",
       "2012-01-20           2  0.217500  0.220958  0.450000   0.202750  3163   \n",
       "2012-01-21           2  0.173333  0.174875  0.831250   0.222642  1301   \n",
       "2012-01-22           2  0.162500  0.162250  0.796250   0.199638  1977   \n",
       "2012-01-23           2  0.218333  0.243058  0.911250   0.110708  2432   \n",
       "2012-01-24           1  0.342500  0.349108  0.835833   0.123767  4339   \n",
       "2012-01-25           1  0.294167  0.294821  0.643750   0.161071  4270   \n",
       "2012-01-26           2  0.341667  0.356050  0.769583   0.073396  4075   \n",
       "2012-01-27           2  0.425000  0.415383  0.741250   0.342667  3456   \n",
       "2012-01-28           1  0.315833  0.326379  0.543333   0.210829  4023   \n",
       "2012-01-29           1  0.282500  0.272721  0.311250   0.240050  3243   \n",
       "2012-01-30           1  0.269167  0.262625  0.400833   0.215792  3624   \n",
       "...                ...       ...       ...       ...        ...   ...   \n",
       "2012-12-02           2  0.347500  0.359208  0.823333   0.124379  4649   \n",
       "2012-12-03           1  0.452500  0.455796  0.767500   0.082721  6234   \n",
       "2012-12-04           1  0.475833  0.469054  0.733750   0.174129  6606   \n",
       "2012-12-05           1  0.438333  0.428012  0.485000   0.324021  5729   \n",
       "2012-12-06           1  0.255833  0.258204  0.508750   0.174754  5375   \n",
       "2012-12-07           2  0.320833  0.321958  0.764167   0.130600  5008   \n",
       "2012-12-08           2  0.381667  0.389508  0.911250   0.101379  5582   \n",
       "2012-12-09           2  0.384167  0.390146  0.905417   0.157975  3228   \n",
       "2012-12-10           2  0.435833  0.435575  0.925000   0.190308  5170   \n",
       "2012-12-11           2  0.353333  0.338363  0.596667   0.296037  5501   \n",
       "2012-12-12           2  0.297500  0.297338  0.538333   0.162937  5319   \n",
       "2012-12-13           1  0.295833  0.294188  0.485833   0.174129  5532   \n",
       "2012-12-14           1  0.281667  0.294192  0.642917   0.131229  5611   \n",
       "2012-12-15           1  0.324167  0.338383  0.650417   0.106350  5047   \n",
       "2012-12-16           2  0.362500  0.369938  0.838750   0.100742  3786   \n",
       "2012-12-17           2  0.393333  0.401500  0.907083   0.098258  4585   \n",
       "2012-12-18           1  0.410833  0.409708  0.666250   0.221404  5557   \n",
       "2012-12-19           1  0.332500  0.342162  0.625417   0.184092  5267   \n",
       "2012-12-20           2  0.330000  0.335217  0.667917   0.132463  4128   \n",
       "2012-12-21           2  0.326667  0.301767  0.556667   0.374383  3623   \n",
       "2012-12-22           1  0.265833  0.236113  0.441250   0.407346  1749   \n",
       "2012-12-23           1  0.245833  0.259471  0.515417   0.133083  1787   \n",
       "2012-12-24           2  0.231304  0.258900  0.791304   0.077230   920   \n",
       "2012-12-25           2  0.291304  0.294465  0.734783   0.168726  1013   \n",
       "2012-12-26           3  0.243333  0.220333  0.823333   0.316546   441   \n",
       "2012-12-27           2  0.254167  0.226642  0.652917   0.350133  2114   \n",
       "2012-12-28           2  0.253333  0.255046  0.590000   0.155471  3095   \n",
       "2012-12-29           2  0.253333  0.242400  0.752917   0.124383  1341   \n",
       "2012-12-30           1  0.255833  0.231700  0.483333   0.350754  1796   \n",
       "2012-12-31           2  0.215833  0.223487  0.577500   0.154846  2729   \n",
       "\n",
       "            season_1  season_2  season_3  season_4  weathersit_1  \\\n",
       "dteday                                                             \n",
       "2012-01-01         1         0         0         0             1   \n",
       "2012-01-02         1         0         0         0             1   \n",
       "2012-01-03         1         0         0         0             1   \n",
       "2012-01-04         1         0         0         0             0   \n",
       "2012-01-05         1         0         0         0             1   \n",
       "2012-01-06         1         0         0         0             1   \n",
       "2012-01-07         1         0         0         0             1   \n",
       "2012-01-08         1         0         0         0             1   \n",
       "2012-01-09         1         0         0         0             0   \n",
       "2012-01-10         1         0         0         0             1   \n",
       "2012-01-11         1         0         0         0             0   \n",
       "2012-01-12         1         0         0         0             0   \n",
       "2012-01-13         1         0         0         0             1   \n",
       "2012-01-14         1         0         0         0             1   \n",
       "2012-01-15         1         0         0         0             1   \n",
       "2012-01-16         1         0         0         0             1   \n",
       "2012-01-17         1         0         0         0             0   \n",
       "2012-01-18         1         0         0         0             1   \n",
       "2012-01-19         1         0         0         0             1   \n",
       "2012-01-20         1         0         0         0             0   \n",
       "2012-01-21         1         0         0         0             0   \n",
       "2012-01-22         1         0         0         0             0   \n",
       "2012-01-23         1         0         0         0             0   \n",
       "2012-01-24         1         0         0         0             1   \n",
       "2012-01-25         1         0         0         0             1   \n",
       "2012-01-26         1         0         0         0             0   \n",
       "2012-01-27         1         0         0         0             0   \n",
       "2012-01-28         1         0         0         0             1   \n",
       "2012-01-29         1         0         0         0             1   \n",
       "2012-01-30         1         0         0         0             1   \n",
       "...              ...       ...       ...       ...           ...   \n",
       "2012-12-02         0         0         0         1             0   \n",
       "2012-12-03         0         0         0         1             1   \n",
       "2012-12-04         0         0         0         1             1   \n",
       "2012-12-05         0         0         0         1             1   \n",
       "2012-12-06         0         0         0         1             1   \n",
       "2012-12-07         0         0         0         1             0   \n",
       "2012-12-08         0         0         0         1             0   \n",
       "2012-12-09         0         0         0         1             0   \n",
       "2012-12-10         0         0         0         1             0   \n",
       "2012-12-11         0         0         0         1             0   \n",
       "2012-12-12         0         0         0         1             0   \n",
       "2012-12-13         0         0         0         1             1   \n",
       "2012-12-14         0         0         0         1             1   \n",
       "2012-12-15         0         0         0         1             1   \n",
       "2012-12-16         0         0         0         1             0   \n",
       "2012-12-17         0         0         0         1             0   \n",
       "2012-12-18         0         0         0         1             1   \n",
       "2012-12-19         0         0         0         1             1   \n",
       "2012-12-20         0         0         0         1             0   \n",
       "2012-12-21         1         0         0         0             0   \n",
       "2012-12-22         1         0         0         0             1   \n",
       "2012-12-23         1         0         0         0             1   \n",
       "2012-12-24         1         0         0         0             0   \n",
       "2012-12-25         1         0         0         0             0   \n",
       "2012-12-26         1         0         0         0             0   \n",
       "2012-12-27         1         0         0         0             0   \n",
       "2012-12-28         1         0         0         0             0   \n",
       "2012-12-29         1         0         0         0             0   \n",
       "2012-12-30         1         0         0         0             1   \n",
       "2012-12-31         1         0         0         0             0   \n",
       "\n",
       "            weathersit_2  weathersit_3  \n",
       "dteday                                  \n",
       "2012-01-01             0             0  \n",
       "2012-01-02             0             0  \n",
       "2012-01-03             0             0  \n",
       "2012-01-04             1             0  \n",
       "2012-01-05             0             0  \n",
       "2012-01-06             0             0  \n",
       "2012-01-07             0             0  \n",
       "2012-01-08             0             0  \n",
       "2012-01-09             1             0  \n",
       "2012-01-10             0             0  \n",
       "2012-01-11             1             0  \n",
       "2012-01-12             1             0  \n",
       "2012-01-13             0             0  \n",
       "2012-01-14             0             0  \n",
       "2012-01-15             0             0  \n",
       "2012-01-16             0             0  \n",
       "2012-01-17             1             0  \n",
       "2012-01-18             0             0  \n",
       "2012-01-19             0             0  \n",
       "2012-01-20             1             0  \n",
       "2012-01-21             1             0  \n",
       "2012-01-22             1             0  \n",
       "2012-01-23             1             0  \n",
       "2012-01-24             0             0  \n",
       "2012-01-25             0             0  \n",
       "2012-01-26             1             0  \n",
       "2012-01-27             1             0  \n",
       "2012-01-28             0             0  \n",
       "2012-01-29             0             0  \n",
       "2012-01-30             0             0  \n",
       "...                  ...           ...  \n",
       "2012-12-02             1             0  \n",
       "2012-12-03             0             0  \n",
       "2012-12-04             0             0  \n",
       "2012-12-05             0             0  \n",
       "2012-12-06             0             0  \n",
       "2012-12-07             1             0  \n",
       "2012-12-08             1             0  \n",
       "2012-12-09             1             0  \n",
       "2012-12-10             1             0  \n",
       "2012-12-11             1             0  \n",
       "2012-12-12             1             0  \n",
       "2012-12-13             0             0  \n",
       "2012-12-14             0             0  \n",
       "2012-12-15             0             0  \n",
       "2012-12-16             1             0  \n",
       "2012-12-17             1             0  \n",
       "2012-12-18             0             0  \n",
       "2012-12-19             0             0  \n",
       "2012-12-20             1             0  \n",
       "2012-12-21             1             0  \n",
       "2012-12-22             0             0  \n",
       "2012-12-23             0             0  \n",
       "2012-12-24             1             0  \n",
       "2012-12-25             1             0  \n",
       "2012-12-26             0             1  \n",
       "2012-12-27             1             0  \n",
       "2012-12-28             1             0  \n",
       "2012-12-29             1             0  \n",
       "2012-12-30             0             0  \n",
       "2012-12-31             1             0  \n",
       "\n",
       "[366 rows x 20 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对测试集类别变量进行处理\n",
    "season1=pd.get_dummies(data_test[\"season\"],prefix=\"season\",columns=\"season\")\n",
    "data_test_d=data_test.join(season1)\n",
    "weathersit1=pd.get_dummies(data_test[\"weathersit\"],prefix=\"weathersit\")\n",
    "data_test_d=data_test_d.join(weathersit1)\n",
    "data_test_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dteday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-02</th>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               atemp       hum  windspeed  season_1  season_2  season_3  \\\n",
       "dteday                                                                    \n",
       "2011-01-01  0.363625  0.805833   0.160446         1         0         0   \n",
       "2011-01-02  0.353739  0.696087   0.248539         1         0         0   \n",
       "2011-01-03  0.189405  0.437273   0.248309         1         0         0   \n",
       "2011-01-04  0.212122  0.590435   0.160296         1         0         0   \n",
       "2011-01-05  0.229270  0.436957   0.186900         1         0         0   \n",
       "\n",
       "            season_4  weathersit_1  weathersit_2  weathersit_3  \n",
       "dteday                                                          \n",
       "2011-01-01         0             0             1             0  \n",
       "2011-01-02         0             0             1             0  \n",
       "2011-01-03         0             1             0             0  \n",
       "2011-01-04         0             1             0             0  \n",
       "2011-01-05         0             1             0             0  "
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取最终训练特征\n",
    "y_train = data_train_d[\"cnt\"].values\n",
    "X_train = data_train_d.drop([\"instant\",\"dteday\",\"holiday\",\"season\",\"mnth\",\"weekday\",\"weathersit\",\"workingday\",\"temp\",\"cnt\"],axis=1)\n",
    "y_test = data_test_d[\"cnt\"].values\n",
    "X_test = data_test_d.drop([\"instant\",\"dteday\",\"holiday\",\"season\",\"mnth\",\"weekday\",\"weathersit\",\"workingday\",\"temp\",\"cnt\"],axis=1)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据归一化\n",
    "X_train[\"atemp\"]=(X_train[\"atemp\"]-X_train[\"atemp\"].min())/(X_train[\"atemp\"].max()-X_train[\"atemp\"].min())\n",
    "X_train[\"windspeed\"]=(X_train[\"windspeed\"]-X_train[\"windspeed\"].min())/(X_train[\"windspeed\"].max()-X_train[\"windspeed\"].min())\n",
    "X_test[\"atemp\"]=(X_test[\"atemp\"]-X_test[\"atemp\"].min())/(X_test[\"atemp\"].max()-X_test[\"atemp\"].min())\n",
    "X_test[\"windspeed\"]=(X_test[\"windspeed\"]-X_test[\"windspeed\"].min())/(X_test[\"windspeed\"].max()-X_test[\"windspeed\"].min())\n",
    "y_train =(y_train-y_train.min())/(y_train.max()-y_train.min())\n",
    "y_test =(y_test-y_test.min())/(y_test.max()-y_test.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=y_train+(y_test.mean()-y_train.mean())#均值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.fit_transform(X_test)\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.fit_transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[0.5646562472127488]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>season_4</td>\n",
       "      <td>[0.1742655450924005]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>[0.09254067183026168]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_2</td>\n",
       "      <td>[0.0647962890850499]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>season_3</td>\n",
       "      <td>[0.04141343210686252]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>[-0.012144191438525588]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.10450108513524636]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.11786894137821777]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>[-0.19739379153794484]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_1</td>\n",
       "      <td>[-0.28089791461180214]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        columns                     coef\n",
       "0         atemp     [0.5646562472127488]\n",
       "6      season_4     [0.1742655450924005]\n",
       "7  weathersit_1    [0.09254067183026168]\n",
       "4      season_2     [0.0647962890850499]\n",
       "5      season_3    [0.04141343210686252]\n",
       "8  weathersit_2  [-0.012144191438525588]\n",
       "1           hum   [-0.10450108513524636]\n",
       "2     windspeed   [-0.11786894137821777]\n",
       "9  weathersit_3   [-0.19739379153794484]\n",
       "3      season_1   [-0.28089791461180214]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#线性回归拟合\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.6997210720078166\n",
      "The r2 score of LinearRegression on train is 0.798943881474363\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0888adcf8>"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f,ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True number')\n",
    "plt.ylabel('Predicted number')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6998879038374597\n",
      "The r2 score of RidgeCV on train is 0.7987958495006574\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "n_alphas = 20\n",
    "alphas = np.logspace(-1,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print( 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 5.455594781168517\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[0.5646562472127488]</td>\n",
       "      <td>[0.5458046004703828]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>season_4</td>\n",
       "      <td>[0.1742655450924005]</td>\n",
       "      <td>[0.16933272996137844]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>[0.09254067183026168]</td>\n",
       "      <td>[0.09378037371913953]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_2</td>\n",
       "      <td>[0.0647962890850499]</td>\n",
       "      <td>[0.06607590976433603]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>season_3</td>\n",
       "      <td>[0.04141343210686252]</td>\n",
       "      <td>[0.049939567705594684]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>[-0.012144191438525588]</td>\n",
       "      <td>[-0.013816589013239379]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.10450108513524636]</td>\n",
       "      <td>[-0.0990758467613003]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.11786894137821777]</td>\n",
       "      <td>[-0.11666822385771979]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>[-0.19739379153794484]</td>\n",
       "      <td>[-0.19643622221610849]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_1</td>\n",
       "      <td>[-0.28089791461180214]</td>\n",
       "      <td>[-0.2859226594670781]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        columns                  coef_lr               coef_ridge\n",
       "0         atemp     [0.5646562472127488]     [0.5458046004703828]\n",
       "6      season_4     [0.1742655450924005]    [0.16933272996137844]\n",
       "7  weathersit_1    [0.09254067183026168]    [0.09378037371913953]\n",
       "4      season_2     [0.0647962890850499]    [0.06607590976433603]\n",
       "5      season_3    [0.04141343210686252]   [0.049939567705594684]\n",
       "8  weathersit_2  [-0.012144191438525588]  [-0.013816589013239379]\n",
       "1           hum   [-0.10450108513524636]    [-0.0990758467613003]\n",
       "2     windspeed   [-0.11786894137821777]   [-0.11666822385771979]\n",
       "9  weathersit_3   [-0.19739379153794484]   [-0.19643622221610849]\n",
       "3      season_1   [-0.28089791461180214]    [-0.2859226594670781]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6988665784275405\n",
      "The r2 score of RidgeCV on train is 0.7967368397439087\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "h:\\python36\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "#alphas = [0.01,0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "n_alphas = 20\n",
    "alphas = np.logspace(-3,0,n_alphas)\n",
    "lasso = LassoCV(alphas=alphas)  \n",
    "#lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print( 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.018329807108324356\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>coef_lasso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[0.5646562472127488]</td>\n",
       "      <td>[0.5458046004703828]</td>\n",
       "      <td>0.523555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>season_4</td>\n",
       "      <td>[0.1742655450924005]</td>\n",
       "      <td>[0.16933272996137844]</td>\n",
       "      <td>0.088271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>[0.09254067183026168]</td>\n",
       "      <td>[0.09378037371913953]</td>\n",
       "      <td>0.109003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_2</td>\n",
       "      <td>[0.0647962890850499]</td>\n",
       "      <td>[0.06607590976433603]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>season_3</td>\n",
       "      <td>[0.04141343210686252]</td>\n",
       "      <td>[0.049939567705594684]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>[-0.012144191438525588]</td>\n",
       "      <td>[-0.013816589013239379]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.10450108513524636]</td>\n",
       "      <td>[-0.0990758467613003]</td>\n",
       "      <td>-0.072746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.11786894137821777]</td>\n",
       "      <td>[-0.11666822385771979]</td>\n",
       "      <td>-0.099855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>[-0.19739379153794484]</td>\n",
       "      <td>[-0.19643622221610849]</td>\n",
       "      <td>-0.182923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_1</td>\n",
       "      <td>[-0.28089791461180214]</td>\n",
       "      <td>[-0.2859226594670781]</td>\n",
       "      <td>-0.347206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        columns                  coef_lr               coef_ridge  coef_lasso\n",
       "0         atemp     [0.5646562472127488]     [0.5458046004703828]    0.523555\n",
       "6      season_4     [0.1742655450924005]    [0.16933272996137844]    0.088271\n",
       "7  weathersit_1    [0.09254067183026168]    [0.09378037371913953]    0.109003\n",
       "4      season_2     [0.0647962890850499]    [0.06607590976433603]    0.000000\n",
       "5      season_3    [0.04141343210686252]   [0.049939567705594684]   -0.000000\n",
       "8  weathersit_2  [-0.012144191438525588]  [-0.013816589013239379]   -0.000000\n",
       "1           hum   [-0.10450108513524636]    [-0.0990758467613003]   -0.072746\n",
       "2     windspeed   [-0.11786894137821777]   [-0.11666822385771979]   -0.099855\n",
       "9  weathersit_3   [-0.19739379153794484]   [-0.19643622221610849]   -0.182923\n",
       "3      season_1   [-0.28089791461180214]    [-0.2859226594670781]   -0.347206"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.018329807108324356\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
